%pip install xgboost
%pip install statsmodels
%pip install pandas numpy statsmodels
Requirement already satisfied: xgboost in c:\users\srume\anaconda3\lib\site-packages (2.0.0) Requirement already satisfied: numpy in c:\users\srume\appdata\roaming\python\python311\site-packages (from xgboost) (1.25.1) Requirement already satisfied: scipy in c:\users\srume\appdata\roaming\python\python311\site-packages (from xgboost) (1.11.1) Note: you may need to restart the kernel to use updated packages. Requirement already satisfied: statsmodels in c:\users\srume\anaconda3\lib\site-packages (0.14.0) Requirement already satisfied: numpy>=1.18 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (1.25.1) Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (1.11.1) Requirement already satisfied: pandas>=1.0 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (2.0.3) Requirement already satisfied: patsy>=0.5.2 in c:\users\srume\anaconda3\lib\site-packages (from statsmodels) (0.5.3) Requirement already satisfied: packaging>=21.3 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (23.1) Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas>=1.0->statsmodels) (2.8.2) Requirement already satisfied: pytz>=2020.1 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas>=1.0->statsmodels) (2023.3) Requirement already satisfied: tzdata>=2022.1 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas>=1.0->statsmodels) (2023.3) Requirement already satisfied: six in c:\users\srume\appdata\roaming\python\python311\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0) Note: you may need to restart the kernel to use updated packages. Requirement already satisfied: pandas in c:\users\srume\appdata\roaming\python\python311\site-packages (2.0.3) Requirement already satisfied: numpy in c:\users\srume\appdata\roaming\python\python311\site-packages (1.25.1) Requirement already satisfied: statsmodels in c:\users\srume\anaconda3\lib\site-packages (0.14.0) Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas) (2.8.2) Requirement already satisfied: pytz>=2020.1 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas) (2023.3) Requirement already satisfied: tzdata>=2022.1 in c:\users\srume\appdata\roaming\python\python311\site-packages (from pandas) (2023.3) Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (1.11.1) Requirement already satisfied: patsy>=0.5.2 in c:\users\srume\anaconda3\lib\site-packages (from statsmodels) (0.5.3) Requirement already satisfied: packaging>=21.3 in c:\users\srume\appdata\roaming\python\python311\site-packages (from statsmodels) (23.1) Requirement already satisfied: six in c:\users\srume\appdata\roaming\python\python311\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0) Note: you may need to restart the kernel to use updated packages.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import scipy.stats as stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import SGDRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.linear_model import Ridge
from xgboost import XGBRegressor
from sklearn.linear_model import Lasso
from statsmodels.tsa.arima.model import ARIMA
from sklearn.linear_model import BayesianRidge
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
import panel as pn
pn.extension()
import hvplot.pandas
from statsmodels.tsa.stattools import adfuller
files available at: https://www.kaggle.com/datasets/ndarshan2797/english-converted-datasets
item_categories.csv - item_category_name, item_category_id
items.csv - item_name, item_id, category_id
sales_train.csv - date, date_block_num, shop_id, item_id, item_price, item_cnt_day
shops.csv - shop_name, shop_id
test.csv - ID, shop_id, item_id
#importing data
item_categories = pd.read_csv('./data-set/item_categories.csv')
items = pd.read_csv('./data-set/items.csv')
sales_train = pd.read_csv('./data-set/sales_train.csv')
shops = pd.read_csv('./data-set/shops.csv')
test = pd.read_csv('./data-set/test.csv')
#checking the shape of the data
print("Shape of item_categories:", item_categories.shape)
print("Shape of items:", items.shape)
print("Shape of sales_train:", sales_train.shape)
print("Shape of shops:", shops.shape)
print("Shape of test:", test.shape)
Shape of item_categories: (84, 2) Shape of items: (22170, 3) Shape of sales_train: (2935849, 6) Shape of shops: (60, 2) Shape of test: (214200, 3)
#checking the columns of the data
print("\n\nColumns of item_categories:\n")
print(item_categories.info())
print("-----------------------------------------------------")
print("\n\nColumns of items:\n")
print(items.info())
print("-----------------------------------------------------")
print("\n\nColumns of sales_train:\n")
print(sales_train.info())
print("-----------------------------------------------------")
print("\n\nColumns of shops:\n")
print(shops.info())
print("-----------------------------------------------------")
print("\n\nColumns of test:\n")
print(test.info())
Columns of item_categories: <class 'pandas.core.frame.DataFrame'> RangeIndex: 84 entries, 0 to 83 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 item_category_name 84 non-null object 1 item_category_id 84 non-null int64 dtypes: int64(1), object(1) memory usage: 1.4+ KB None ----------------------------------------------------- Columns of items: <class 'pandas.core.frame.DataFrame'> RangeIndex: 22170 entries, 0 to 22169 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 item_name 22170 non-null object 1 item_id 22170 non-null int64 2 category_id 22170 non-null int64 dtypes: int64(2), object(1) memory usage: 519.7+ KB None ----------------------------------------------------- Columns of sales_train: <class 'pandas.core.frame.DataFrame'> RangeIndex: 2935849 entries, 0 to 2935848 Data columns (total 6 columns): # Column Dtype --- ------ ----- 0 date object 1 date_block_num int64 2 shop_id int64 3 item_id int64 4 item_price float64 5 item_cnt_day float64 dtypes: float64(2), int64(3), object(1) memory usage: 134.4+ MB None ----------------------------------------------------- Columns of shops: <class 'pandas.core.frame.DataFrame'> RangeIndex: 60 entries, 0 to 59 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 shop_name 60 non-null object 1 shop_id 60 non-null int64 dtypes: int64(1), object(1) memory usage: 1.1+ KB None ----------------------------------------------------- Columns of test: <class 'pandas.core.frame.DataFrame'> RangeIndex: 214200 entries, 0 to 214199 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 214200 non-null int64 1 shop_id 214200 non-null int64 2 item_id 214200 non-null int64 dtypes: int64(3) memory usage: 4.9 MB None
#checking the head and tail of the data
print("\n\nHead of item_categories:\n")
print(item_categories.head())
print("\n\nTail of item_categories:\n")
print(item_categories.tail())
print("-----------------------------------------------------")
print("\n\nHead of items:\n")
print(items.head())
print("\n\nTail of items:\n")
print(items.tail())
print("-----------------------------------------------------")
print("\n\nHead of sales_train:\n")
print(sales_train.head())
print("\n\nTail of sales_train:\n")
print(sales_train.tail())
print("-----------------------------------------------------")
print("\n\nHead of shops:\n")
print(shops.head())
print("\n\nTail of shops:\n")
print(shops.tail())
print("-----------------------------------------------------")
print("\n\nHead of test:\n")
print(test.head())
print("\n\nTail of test:\n")
print(test.tail())
Head of item_categories:
item_category_name item_category_id
0 PC - Headsets / Headphones 0
1 Accessories - PS2 1
2 Accessories - PS3 2
3 Accessories - PS4 3
4 Accessories - PSP 4
Tail of item_categories:
item_category_name item_category_id
79 Service 79
80 Service - Tickets 80
81 Blank media (spire) 81
82 Blank media (piece) 82
83 Batteries 83
-----------------------------------------------------
Head of items:
item_name item_id category_id
0 !! IN THE POWER OF HAPPINESS (PLAST) D 0 40
1 ! ABBYY FineReader 12 Professional Edition Ful... 1 76
2 *** IN THE GLORY OF THE GLORY (UNV) D 2 40
3 *** BLUE WAVE (Univ) D 3 40
4 *** BOX (GLASS) D 4 40
Tail of items:
item_name item_id category_id
22165 Nuclear Titbit 2 [PC, Digital Version] 22165 31
22166 Query language 1C: Enterprise [Digital version] 22166 54
22167 The query language is 1C: Enterprise 8 (+ CD).... 22167 49
22168 Egg for Little Inu 22168 62
22169 Egg of the Dragon (Game of Thrones) 22169 69
-----------------------------------------------------
Head of sales_train:
date date_block_num shop_id item_id item_price item_cnt_day
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
Tail of sales_train:
date date_block_num shop_id item_id item_price \
2935844 10.10.2015 33 25 7409 299.0
2935845 09.10.2015 33 25 7460 299.0
2935846 14.10.2015 33 25 7459 349.0
2935847 22.10.2015 33 25 7440 299.0
2935848 03.10.2015 33 25 7460 299.0
item_cnt_day
2935844 1.0
2935845 1.0
2935846 1.0
2935847 1.0
2935848 1.0
-----------------------------------------------------
Head of shops:
shop_name shop_id
0 ! Yakutsk Ordzhonikidze, 56 francs 0
1 ! Yakutsk TC "Central" fran 1
2 Adygea TC "Mega" 2
3 Balashikha TC "Oktyabr-Kinomir" 3
4 Volga TC "Volga Mall" 4
Tail of shops:
shop_name shop_id
55 Digital warehouse 1C-Online 55
56 Chekhov TC" Karnaval " 56
57 Yakutsk Ordzhonikidze, 56 57
58 Yakutsk TC" Central " 58
59 Yaroslavl TC" Altair " 59
-----------------------------------------------------
Head of test:
ID shop_id item_id
0 0 5 5037
1 1 5 5320
2 2 5 5233
3 3 5 5232
4 4 5 5268
Tail of test:
ID shop_id item_id
214195 214195 45 18454
214196 214196 45 16188
214197 214197 45 15757
214198 214198 45 19648
214199 214199 45 969
#merging the data for better understand the data
#Merge sales_train.csv with items.csv on the "item_id" column
sales_with_items = sales_train.merge(items, on='item_id', how='left')
print("\n\nHead of sales_with_items:\n")
print(sales_with_items.head(20))
print(sales_with_items.shape)
Head of sales_with_items:
date date_block_num shop_id item_id item_price item_cnt_day \
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
10 03.01.2013 0 25 2574 399.00 2.0
11 05.01.2013 0 25 2574 399.00 1.0
12 07.01.2013 0 25 2574 399.00 1.0
13 08.01.2013 0 25 2574 399.00 2.0
14 10.01.2013 0 25 2574 399.00 1.0
15 11.01.2013 0 25 2574 399.00 2.0
16 13.01.2013 0 25 2574 399.00 1.0
17 16.01.2013 0 25 2574 399.00 1.0
18 26.01.2013 0 25 2574 399.00 1.0
19 27.01.2013 0 25 2574 399.00 1.0
item_name category_id
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
2 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
(2935849, 8)
#Merge the result with item_categories.csv on the "category_id"
sales_with_items_and_categories = sales_with_items.merge(item_categories, right_on='item_category_id', left_on='category_id', how='left')
print("\n\nHead of sales_with_items_and_categories:\n")
print(sales_with_items_and_categories.head(20))
print(sales_with_items_and_categories.shape)
Head of sales_with_items_and_categories:
date date_block_num shop_id item_id item_price item_cnt_day \
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
10 03.01.2013 0 25 2574 399.00 2.0
11 05.01.2013 0 25 2574 399.00 1.0
12 07.01.2013 0 25 2574 399.00 1.0
13 08.01.2013 0 25 2574 399.00 2.0
14 10.01.2013 0 25 2574 399.00 1.0
15 11.01.2013 0 25 2574 399.00 2.0
16 13.01.2013 0 25 2574 399.00 1.0
17 16.01.2013 0 25 2574 399.00 1.0
18 26.01.2013 0 25 2574 399.00 1.0
19 27.01.2013 0 25 2574 399.00 1.0
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
2 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
item_category_name item_category_id
0 Cinema - Blu-Ray 37
1 Music - Vinyl 58
2 Music - Vinyl 58
3 Music - Vinyl 58
4 Music - CD of corporate production 56
5 Music - Music video 59
6 Music - CD of corporate production 56
7 Music - CD of local production 55
8 Music - CD of local production 55
9 Music - CD of local production 55
10 Music - CD of local production 55
11 Music - CD of local production 55
12 Music - CD of local production 55
13 Music - CD of local production 55
14 Music - CD of local production 55
15 Music - CD of local production 55
16 Music - CD of local production 55
17 Music - CD of local production 55
18 Music - CD of local production 55
19 Music - CD of local production 55
(2935849, 10)
# Check if the two columns are the same
if sales_with_items_and_categories['item_category_id'].equals(sales_with_items_and_categories['category_id']):
# If they are the same, drop one of the columns
sales_with_items_and_categories.drop(columns=['item_category_id'], inplace=True)
print("\n\nHead of sales_with_items_and_categories:\n")
print(sales_with_items_and_categories.head(20))
print(sales_with_items_and_categories.shape)
Head of sales_with_items_and_categories:
date date_block_num shop_id item_id item_price item_cnt_day \
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
10 03.01.2013 0 25 2574 399.00 2.0
11 05.01.2013 0 25 2574 399.00 1.0
12 07.01.2013 0 25 2574 399.00 1.0
13 08.01.2013 0 25 2574 399.00 2.0
14 10.01.2013 0 25 2574 399.00 1.0
15 11.01.2013 0 25 2574 399.00 2.0
16 13.01.2013 0 25 2574 399.00 1.0
17 16.01.2013 0 25 2574 399.00 1.0
18 26.01.2013 0 25 2574 399.00 1.0
19 27.01.2013 0 25 2574 399.00 1.0
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
2 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
item_category_name
0 Cinema - Blu-Ray
1 Music - Vinyl
2 Music - Vinyl
3 Music - Vinyl
4 Music - CD of corporate production
5 Music - Music video
6 Music - CD of corporate production
7 Music - CD of local production
8 Music - CD of local production
9 Music - CD of local production
10 Music - CD of local production
11 Music - CD of local production
12 Music - CD of local production
13 Music - CD of local production
14 Music - CD of local production
15 Music - CD of local production
16 Music - CD of local production
17 Music - CD of local production
18 Music - CD of local production
19 Music - CD of local production
(2935849, 9)
#Merge the result with shops.csv on the "shop_id"
final_dataset = sales_with_items_and_categories.merge(shops, on='shop_id', how='left')
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_block_num shop_id item_id item_price item_cnt_day \
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
10 03.01.2013 0 25 2574 399.00 2.0
11 05.01.2013 0 25 2574 399.00 1.0
12 07.01.2013 0 25 2574 399.00 1.0
13 08.01.2013 0 25 2574 399.00 2.0
14 10.01.2013 0 25 2574 399.00 1.0
15 11.01.2013 0 25 2574 399.00 2.0
16 13.01.2013 0 25 2574 399.00 1.0
17 16.01.2013 0 25 2574 399.00 1.0
18 26.01.2013 0 25 2574 399.00 1.0
19 27.01.2013 0 25 2574 399.00 1.0
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
2 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
item_category_name shop_name
0 Cinema - Blu-Ray Yaroslavl TC" Altair "
1 Music - Vinyl Moscow TEC" Atrium "
2 Music - Vinyl Moscow TEC" Atrium "
3 Music - Vinyl Moscow TEC" Atrium "
4 Music - CD of corporate production Moscow TEC" Atrium "
5 Music - Music video Moscow TEC" Atrium "
6 Music - CD of corporate production Moscow TEC" Atrium "
7 Music - CD of local production Moscow TEC" Atrium "
8 Music - CD of local production Moscow TEC" Atrium "
9 Music - CD of local production Moscow TEC" Atrium "
10 Music - CD of local production Moscow TEC" Atrium "
11 Music - CD of local production Moscow TEC" Atrium "
12 Music - CD of local production Moscow TEC" Atrium "
13 Music - CD of local production Moscow TEC" Atrium "
14 Music - CD of local production Moscow TEC" Atrium "
15 Music - CD of local production Moscow TEC" Atrium "
16 Music - CD of local production Moscow TEC" Atrium "
17 Music - CD of local production Moscow TEC" Atrium "
18 Music - CD of local production Moscow TEC" Atrium "
19 Music - CD of local production Moscow TEC" Atrium "
(2935849, 10)
#checks the columns of the final dataset
print("\n\nColumns of final_dataset:\n")
print(final_dataset.info())
Columns of final_dataset: <class 'pandas.core.frame.DataFrame'> RangeIndex: 2935849 entries, 0 to 2935848 Data columns (total 10 columns): # Column Dtype --- ------ ----- 0 date object 1 date_block_num int64 2 shop_id int64 3 item_id int64 4 item_price float64 5 item_cnt_day float64 6 item_name object 7 category_id int64 8 item_category_name object 9 shop_name object dtypes: float64(2), int64(4), object(4) memory usage: 224.0+ MB None
#prints the date and date_block_num column to check whether they are related
columns_to_print = ['date', 'date_block_num']
print(final_dataset[columns_to_print])
date date_block_num 0 02.01.2013 0 1 03.01.2013 0 2 05.01.2013 0 3 06.01.2013 0 4 15.01.2013 0 ... ... ... 2935844 10.10.2015 33 2935845 09.10.2015 33 2935846 14.10.2015 33 2935847 22.10.2015 33 2935848 03.10.2015 33 [2935849 rows x 2 columns]
# Rename the column
final_dataset.rename(columns={'date_block_num': 'month_num'}, inplace=True)
#Rename the column
final_dataset.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
10 03.01.2013 0 25 2574 399.00 2.0
11 05.01.2013 0 25 2574 399.00 1.0
12 07.01.2013 0 25 2574 399.00 1.0
13 08.01.2013 0 25 2574 399.00 2.0
14 10.01.2013 0 25 2574 399.00 1.0
15 11.01.2013 0 25 2574 399.00 2.0
16 13.01.2013 0 25 2574 399.00 1.0
17 16.01.2013 0 25 2574 399.00 1.0
18 26.01.2013 0 25 2574 399.00 1.0
19 27.01.2013 0 25 2574 399.00 1.0
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
2 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
item_category_name shop_name
0 Cinema - Blu-Ray Yaroslavl TC" Altair "
1 Music - Vinyl Moscow TEC" Atrium "
2 Music - Vinyl Moscow TEC" Atrium "
3 Music - Vinyl Moscow TEC" Atrium "
4 Music - CD of corporate production Moscow TEC" Atrium "
5 Music - Music video Moscow TEC" Atrium "
6 Music - CD of corporate production Moscow TEC" Atrium "
7 Music - CD of local production Moscow TEC" Atrium "
8 Music - CD of local production Moscow TEC" Atrium "
9 Music - CD of local production Moscow TEC" Atrium "
10 Music - CD of local production Moscow TEC" Atrium "
11 Music - CD of local production Moscow TEC" Atrium "
12 Music - CD of local production Moscow TEC" Atrium "
13 Music - CD of local production Moscow TEC" Atrium "
14 Music - CD of local production Moscow TEC" Atrium "
15 Music - CD of local production Moscow TEC" Atrium "
16 Music - CD of local production Moscow TEC" Atrium "
17 Music - CD of local production Moscow TEC" Atrium "
18 Music - CD of local production Moscow TEC" Atrium "
19 Music - CD of local production Moscow TEC" Atrium "
(2935849, 10)
#checks the columns of the final dataset
print("\n\nColumns of final_dataset:\n")
print(final_dataset.info())
Columns of final_dataset: <class 'pandas.core.frame.DataFrame'> RangeIndex: 2935849 entries, 0 to 2935848 Data columns (total 10 columns): # Column Dtype --- ------ ----- 0 date object 1 month_num int64 2 shop_id int64 3 item_id int64 4 item_price float64 5 item_cnt_month float64 6 item_name object 7 category_id int64 8 item_category_name object 9 shop_name object dtypes: float64(2), int64(4), object(4) memory usage: 224.0+ MB None
#export the final dataset to csv file
final_dataset.to_csv('./data-set/output/final_dataset_without_cleaning.csv', index=False)
#Data Cleaning
#checking for missing values
print("\n\nMissing values in final_dataset:\n")
print(final_dataset.isnull().sum())
Missing values in final_dataset: date 0 month_num 0 shop_id 0 item_id 0 item_price 0 item_cnt_month 0 item_name 0 category_id 0 item_category_name 0 shop_name 0 dtype: int64
#checking for null values
print("\n\nNull values in final_dataset:\n")
print(final_dataset.isnull().sum())
Null values in final_dataset: date 0 month_num 0 shop_id 0 item_id 0 item_price 0 item_cnt_month 0 item_name 0 category_id 0 item_category_name 0 shop_name 0 dtype: int64
print(final_dataset.shape)
(2935849, 10)
#handles the missing values in final_dataset
final_dataset['item_name'].fillna('Unknown', inplace=True)
final_dataset['item_category_name'].fillna('Unknown', inplace=True)
print(final_dataset.shape)
(2935849, 10)
#removes duplicates rows in final_dataset
final_dataset.drop_duplicates(inplace=True)
print(final_dataset.shape)
(2935843, 10)
#checks and solves the data type of the columns
print("\n\nData types of final_dataset:\n")
print(final_dataset.dtypes)
Data types of final_dataset: date object month_num int64 shop_id int64 item_id int64 item_price float64 item_cnt_month float64 item_name object category_id int64 item_category_name object shop_name object dtype: object
# #seems like item_cnt_month should be int64
final_dataset['item_cnt_month'] = final_dataset['item_cnt_month'].astype('int64')
print(final_dataset.dtypes)
date object month_num int64 shop_id int64 item_id int64 item_price float64 item_cnt_month int64 item_name object category_id int64 item_category_name object shop_name object dtype: object
#prints item_cnt_month column to check whether it is int64
print(final_dataset['item_cnt_month'].head(30))
0 1 1 1 2 -1 3 1 4 1 5 1 6 1 7 1 8 1 9 3 10 2 11 1 12 1 13 2 14 1 15 2 16 1 17 1 18 1 19 1 20 1 21 1 22 1 23 1 24 1 25 1 26 1 27 1 28 1 29 1 Name: item_cnt_month, dtype: int64
print(final_dataset.shape)
(2935843, 10)
#removes -1 and 307980 from item_cnt_month column because it is an outlier
#it is not possible to sell -1 and 307980 items in a day because 307980 is the total number of items sold in a day
#which means that the data is incorrect
#and -1 is not possible
final_dataset = final_dataset[(final_dataset['item_cnt_month'] > 0) & (final_dataset['item_cnt_month'] < 307980)]
print(final_dataset.shape)
(2928487, 10)
#outlier treatment
#checks for outliers in the item_cnt_month column
print("\n\nOutliers in item_cnt_month column:\n")
print(final_dataset[final_dataset['item_cnt_month'] > 1000])
#removes the outliers in the item_cnt_month column
final_dataset = final_dataset[final_dataset['item_cnt_month'] < 1000]
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Outliers in item_cnt_month column:
date month_num shop_id item_id item_price item_cnt_month \
2909818 28.10.2015 33 12 11373 0.908714 2169
item_name category_id \
2909818 Delivery to the point of issue (Boxberry) 9
item_category_name shop_name
2909818 Delivery of goods Internet-shop of emergency situations
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Editio... 55
11 DEL REY LANA Born To Die The Paradise Editio... 55
12 DEL REY LANA Born To Die The Paradise Editio... 55
13 DEL REY LANA Born To Die The Paradise Editio... 55
14 DEL REY LANA Born To Die The Paradise Editio... 55
15 DEL REY LANA Born To Die The Paradise Editio... 55
16 DEL REY LANA Born To Die The Paradise Editio... 55
17 DEL REY LANA Born To Die The Paradise Editio... 55
18 DEL REY LANA Born To Die The Paradise Editio... 55
19 DEL REY LANA Born To Die The Paradise Editio... 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name
0 Cinema - Blu-Ray Yaroslavl TC" Altair "
1 Music - Vinyl Moscow TEC" Atrium "
3 Music - Vinyl Moscow TEC" Atrium "
4 Music - CD of corporate production Moscow TEC" Atrium "
5 Music - Music video Moscow TEC" Atrium "
6 Music - CD of corporate production Moscow TEC" Atrium "
7 Music - CD of local production Moscow TEC" Atrium "
8 Music - CD of local production Moscow TEC" Atrium "
9 Music - CD of local production Moscow TEC" Atrium "
10 Music - CD of local production Moscow TEC" Atrium "
11 Music - CD of local production Moscow TEC" Atrium "
12 Music - CD of local production Moscow TEC" Atrium "
13 Music - CD of local production Moscow TEC" Atrium "
14 Music - CD of local production Moscow TEC" Atrium "
15 Music - CD of local production Moscow TEC" Atrium "
16 Music - CD of local production Moscow TEC" Atrium "
17 Music - CD of local production Moscow TEC" Atrium "
18 Music - CD of local production Moscow TEC" Atrium "
19 Music - CD of local production Moscow TEC" Atrium "
20 Music - CD of local production Moscow TEC" Atrium "
(2928485, 10)
#deal with the incorrect data in the item_price column
#the item_price should not be negative
#the item_price should not be zero
#the item_price should not be greater than 100000
final_dataset = final_dataset[(final_dataset['item_price'] > 0) & (final_dataset['item_price'] < 100000)]
print(final_dataset.shape)
(2928483, 10)
#handles special characters and formatting in the data set
final_dataset['item_name'] = final_dataset['item_name'].str.replace('[^A-Za-z0-9А-Яа-я]+', ' ')
print(final_dataset.shape)
(2928483, 10)
#removes the noise in the item_name column
final_dataset['item_name'] = final_dataset['item_name'].str.replace(' ', ' ')
print(final_dataset.head())
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
item_category_name shop_name
0 Cinema - Blu-Ray Yaroslavl TC" Altair "
1 Music - Vinyl Moscow TEC" Atrium "
3 Music - Vinyl Moscow TEC" Atrium "
4 Music - CD of corporate production Moscow TEC" Atrium "
5 Music - Music video Moscow TEC" Atrium "
#creates a new column called revenue
final_dataset['revenue'] = final_dataset['item_cnt_month'] * final_dataset['item_price']
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name revenue
0 Cinema - Blu-Ray Yaroslavl TC" Altair " 999.00
1 Music - Vinyl Moscow TEC" Atrium " 899.00
3 Music - Vinyl Moscow TEC" Atrium " 1709.05
4 Music - CD of corporate production Moscow TEC" Atrium " 1099.00
5 Music - Music video Moscow TEC" Atrium " 349.00
6 Music - CD of corporate production Moscow TEC" Atrium " 549.00
7 Music - CD of local production Moscow TEC" Atrium " 239.00
8 Music - CD of local production Moscow TEC" Atrium " 299.00
9 Music - CD of local production Moscow TEC" Atrium " 897.00
10 Music - CD of local production Moscow TEC" Atrium " 798.00
11 Music - CD of local production Moscow TEC" Atrium " 399.00
12 Music - CD of local production Moscow TEC" Atrium " 399.00
13 Music - CD of local production Moscow TEC" Atrium " 798.00
14 Music - CD of local production Moscow TEC" Atrium " 399.00
15 Music - CD of local production Moscow TEC" Atrium " 798.00
16 Music - CD of local production Moscow TEC" Atrium " 399.00
17 Music - CD of local production Moscow TEC" Atrium " 399.00
18 Music - CD of local production Moscow TEC" Atrium " 399.00
19 Music - CD of local production Moscow TEC" Atrium " 399.00
20 Music - CD of local production Moscow TEC" Atrium " 279.00
(2928483, 11)
#creates a new column called revenue_per_item
final_dataset['revenue_per_item'] = final_dataset['revenue'] / final_dataset['item_cnt_month']
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name revenue \
0 Cinema - Blu-Ray Yaroslavl TC" Altair " 999.00
1 Music - Vinyl Moscow TEC" Atrium " 899.00
3 Music - Vinyl Moscow TEC" Atrium " 1709.05
4 Music - CD of corporate production Moscow TEC" Atrium " 1099.00
5 Music - Music video Moscow TEC" Atrium " 349.00
6 Music - CD of corporate production Moscow TEC" Atrium " 549.00
7 Music - CD of local production Moscow TEC" Atrium " 239.00
8 Music - CD of local production Moscow TEC" Atrium " 299.00
9 Music - CD of local production Moscow TEC" Atrium " 897.00
10 Music - CD of local production Moscow TEC" Atrium " 798.00
11 Music - CD of local production Moscow TEC" Atrium " 399.00
12 Music - CD of local production Moscow TEC" Atrium " 399.00
13 Music - CD of local production Moscow TEC" Atrium " 798.00
14 Music - CD of local production Moscow TEC" Atrium " 399.00
15 Music - CD of local production Moscow TEC" Atrium " 798.00
16 Music - CD of local production Moscow TEC" Atrium " 399.00
17 Music - CD of local production Moscow TEC" Atrium " 399.00
18 Music - CD of local production Moscow TEC" Atrium " 399.00
19 Music - CD of local production Moscow TEC" Atrium " 399.00
20 Music - CD of local production Moscow TEC" Atrium " 279.00
revenue_per_item
0 999.00
1 899.00
3 1709.05
4 1099.00
5 349.00
6 549.00
7 239.00
8 299.00
9 299.00
10 399.00
11 399.00
12 399.00
13 399.00
14 399.00
15 399.00
16 399.00
17 399.00
18 399.00
19 399.00
20 279.00
(2928483, 12)
#checks whether the revenue_per_item column and revenue column are the same
if final_dataset['revenue_per_item'].equals(final_dataset['revenue']):
# If they are the same, drop one of the columns
final_dataset.drop(columns=['revenue_per_item'], inplace=True)
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name revenue \
0 Cinema - Blu-Ray Yaroslavl TC" Altair " 999.00
1 Music - Vinyl Moscow TEC" Atrium " 899.00
3 Music - Vinyl Moscow TEC" Atrium " 1709.05
4 Music - CD of corporate production Moscow TEC" Atrium " 1099.00
5 Music - Music video Moscow TEC" Atrium " 349.00
6 Music - CD of corporate production Moscow TEC" Atrium " 549.00
7 Music - CD of local production Moscow TEC" Atrium " 239.00
8 Music - CD of local production Moscow TEC" Atrium " 299.00
9 Music - CD of local production Moscow TEC" Atrium " 897.00
10 Music - CD of local production Moscow TEC" Atrium " 798.00
11 Music - CD of local production Moscow TEC" Atrium " 399.00
12 Music - CD of local production Moscow TEC" Atrium " 399.00
13 Music - CD of local production Moscow TEC" Atrium " 798.00
14 Music - CD of local production Moscow TEC" Atrium " 399.00
15 Music - CD of local production Moscow TEC" Atrium " 798.00
16 Music - CD of local production Moscow TEC" Atrium " 399.00
17 Music - CD of local production Moscow TEC" Atrium " 399.00
18 Music - CD of local production Moscow TEC" Atrium " 399.00
19 Music - CD of local production Moscow TEC" Atrium " 399.00
20 Music - CD of local production Moscow TEC" Atrium " 279.00
revenue_per_item
0 999.00
1 899.00
3 1709.05
4 1099.00
5 349.00
6 549.00
7 239.00
8 299.00
9 299.00
10 399.00
11 399.00
12 399.00
13 399.00
14 399.00
15 399.00
16 399.00
17 399.00
18 399.00
19 399.00
20 279.00
(2928483, 12)
#creates a new column called date num
final_dataset['date_num'] = final_dataset['date'].str[:2]
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name revenue \
0 Cinema - Blu-Ray Yaroslavl TC" Altair " 999.00
1 Music - Vinyl Moscow TEC" Atrium " 899.00
3 Music - Vinyl Moscow TEC" Atrium " 1709.05
4 Music - CD of corporate production Moscow TEC" Atrium " 1099.00
5 Music - Music video Moscow TEC" Atrium " 349.00
6 Music - CD of corporate production Moscow TEC" Atrium " 549.00
7 Music - CD of local production Moscow TEC" Atrium " 239.00
8 Music - CD of local production Moscow TEC" Atrium " 299.00
9 Music - CD of local production Moscow TEC" Atrium " 897.00
10 Music - CD of local production Moscow TEC" Atrium " 798.00
11 Music - CD of local production Moscow TEC" Atrium " 399.00
12 Music - CD of local production Moscow TEC" Atrium " 399.00
13 Music - CD of local production Moscow TEC" Atrium " 798.00
14 Music - CD of local production Moscow TEC" Atrium " 399.00
15 Music - CD of local production Moscow TEC" Atrium " 798.00
16 Music - CD of local production Moscow TEC" Atrium " 399.00
17 Music - CD of local production Moscow TEC" Atrium " 399.00
18 Music - CD of local production Moscow TEC" Atrium " 399.00
19 Music - CD of local production Moscow TEC" Atrium " 399.00
20 Music - CD of local production Moscow TEC" Atrium " 279.00
revenue_per_item date_num
0 999.00 02
1 899.00 03
3 1709.05 06
4 1099.00 15
5 349.00 10
6 549.00 02
7 239.00 04
8 299.00 11
9 299.00 03
10 399.00 03
11 399.00 05
12 399.00 07
13 399.00 08
14 399.00 10
15 399.00 11
16 399.00 13
17 399.00 16
18 399.00 26
19 399.00 27
20 279.00 09
(2928483, 13)
#creates a new column called year num
final_dataset['year_num'] = final_dataset['date'].str[6:]
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date month_num shop_id item_id item_price item_cnt_month \
0 02.01.2013 0 59 22154 999.00 1
1 03.01.2013 0 25 2552 899.00 1
3 06.01.2013 0 25 2554 1709.05 1
4 15.01.2013 0 25 2555 1099.00 1
5 10.01.2013 0 25 2564 349.00 1
6 02.01.2013 0 25 2565 549.00 1
7 04.01.2013 0 25 2572 239.00 1
8 11.01.2013 0 25 2572 299.00 1
9 03.01.2013 0 25 2573 299.00 3
10 03.01.2013 0 25 2574 399.00 2
11 05.01.2013 0 25 2574 399.00 1
12 07.01.2013 0 25 2574 399.00 1
13 08.01.2013 0 25 2574 399.00 2
14 10.01.2013 0 25 2574 399.00 1
15 11.01.2013 0 25 2574 399.00 2
16 13.01.2013 0 25 2574 399.00 1
17 16.01.2013 0 25 2574 399.00 1
18 26.01.2013 0 25 2574 399.00 1
19 27.01.2013 0 25 2574 399.00 1
20 09.01.2013 0 25 2593 279.00 1
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name shop_name revenue \
0 Cinema - Blu-Ray Yaroslavl TC" Altair " 999.00
1 Music - Vinyl Moscow TEC" Atrium " 899.00
3 Music - Vinyl Moscow TEC" Atrium " 1709.05
4 Music - CD of corporate production Moscow TEC" Atrium " 1099.00
5 Music - Music video Moscow TEC" Atrium " 349.00
6 Music - CD of corporate production Moscow TEC" Atrium " 549.00
7 Music - CD of local production Moscow TEC" Atrium " 239.00
8 Music - CD of local production Moscow TEC" Atrium " 299.00
9 Music - CD of local production Moscow TEC" Atrium " 897.00
10 Music - CD of local production Moscow TEC" Atrium " 798.00
11 Music - CD of local production Moscow TEC" Atrium " 399.00
12 Music - CD of local production Moscow TEC" Atrium " 399.00
13 Music - CD of local production Moscow TEC" Atrium " 798.00
14 Music - CD of local production Moscow TEC" Atrium " 399.00
15 Music - CD of local production Moscow TEC" Atrium " 798.00
16 Music - CD of local production Moscow TEC" Atrium " 399.00
17 Music - CD of local production Moscow TEC" Atrium " 399.00
18 Music - CD of local production Moscow TEC" Atrium " 399.00
19 Music - CD of local production Moscow TEC" Atrium " 399.00
20 Music - CD of local production Moscow TEC" Atrium " 279.00
revenue_per_item date_num year_num
0 999.00 02 2013
1 899.00 03 2013
3 1709.05 06 2013
4 1099.00 15 2013
5 349.00 10 2013
6 549.00 02 2013
7 239.00 04 2013
8 299.00 11 2013
9 299.00 03 2013
10 399.00 03 2013
11 399.00 05 2013
12 399.00 07 2013
13 399.00 08 2013
14 399.00 10 2013
15 399.00 11 2013
16 399.00 13 2013
17 399.00 16 2013
18 399.00 26 2013
19 399.00 27 2013
20 279.00 09 2013
(2928483, 14)
print(final_dataset.shape)
print(final_dataset.info())
(2928483, 14) <class 'pandas.core.frame.DataFrame'> Index: 2928483 entries, 0 to 2935848 Data columns (total 14 columns): # Column Dtype --- ------ ----- 0 date object 1 month_num int64 2 shop_id int64 3 item_id int64 4 item_price float64 5 item_cnt_month int64 6 item_name object 7 category_id int64 8 item_category_name object 9 shop_name object 10 revenue float64 11 revenue_per_item float64 12 date_num object 13 year_num object dtypes: float64(3), int64(5), object(6) memory usage: 335.1+ MB None
# rearrange the columns
final_dataset = final_dataset[['date', 'date_num', 'year_num', 'month_num', 'shop_id', 'shop_name', 'item_id', 'item_name', 'category_id', 'item_category_name', 'item_price', 'item_cnt_month', 'revenue']]
print(final_dataset.shape)
print(final_dataset.info())
(2928483, 13) <class 'pandas.core.frame.DataFrame'> Index: 2928483 entries, 0 to 2935848 Data columns (total 13 columns): # Column Dtype --- ------ ----- 0 date object 1 date_num object 2 year_num object 3 month_num int64 4 shop_id int64 5 shop_name object 6 item_id int64 7 item_name object 8 category_id int64 9 item_category_name object 10 item_price float64 11 item_cnt_month int64 12 revenue float64 dtypes: float64(2), int64(5), object(6) memory usage: 312.8+ MB None
#data profiling
#descriptive statistics
print("\n\nDescriptive statistics of final_dataset:\n")
print(final_dataset.describe())
Descriptive statistics of final_dataset:
month_num shop_id item_id category_id item_price \
count 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06
mean 1.456976e+01 3.300296e+01 1.020028e+04 4.001637e+01 8.893627e+02
std 9.422952e+00 1.622543e+01 6.324391e+03 1.709809e+01 1.718155e+03
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 7.000000e-02
25% 7.000000e+00 2.200000e+01 4.477000e+03 2.800000e+01 2.490000e+02
50% 1.400000e+01 3.100000e+01 9.355000e+03 4.000000e+01 3.990000e+02
75% 2.300000e+01 4.700000e+01 1.569100e+04 5.500000e+01 9.990000e+02
max 3.300000e+01 5.900000e+01 2.216900e+04 8.300000e+01 5.920000e+04
item_cnt_month revenue
count 2.928483e+06 2.928483e+06
mean 1.247257e+00 1.164267e+03
std 2.217429e+00 5.684853e+03
min 1.000000e+00 7.000000e-02
25% 1.000000e+00 2.490000e+02
50% 1.000000e+00 4.490000e+02
75% 1.000000e+00 1.090000e+03
max 6.690000e+02 1.829990e+06
#data enrichment
#creates a new column called month name
final_dataset['month_name'] = final_dataset['month_num'].replace({0: 'January', 1: 'February', 2: 'March', 3: 'April', 4: 'May', 5: 'June', 6: 'July', 7: 'August', 8: 'September', 9: 'October', 10: 'November', 11: 'December', 12: 'January', 13: 'February', 14: 'March', 15: 'April', 16: 'May', 17: 'June', 18: 'July', 19: 'August', 20: 'September', 21: 'October', 22: 'November', 23: 'December', 24: 'January', 25: 'February', 26: 'March', 27: 'April', 28: 'May', 29: 'June', 30: 'July', 31: 'August', 32: 'September', 33: 'October'})
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num year_num month_num shop_id shop_name \
0 02.01.2013 02 2013 0 59 Yaroslavl TC" Altair "
1 03.01.2013 03 2013 0 25 Moscow TEC" Atrium "
3 06.01.2013 06 2013 0 25 Moscow TEC" Atrium "
4 15.01.2013 15 2013 0 25 Moscow TEC" Atrium "
5 10.01.2013 10 2013 0 25 Moscow TEC" Atrium "
6 02.01.2013 02 2013 0 25 Moscow TEC" Atrium "
7 04.01.2013 04 2013 0 25 Moscow TEC" Atrium "
8 11.01.2013 11 2013 0 25 Moscow TEC" Atrium "
9 03.01.2013 03 2013 0 25 Moscow TEC" Atrium "
10 03.01.2013 03 2013 0 25 Moscow TEC" Atrium "
11 05.01.2013 05 2013 0 25 Moscow TEC" Atrium "
12 07.01.2013 07 2013 0 25 Moscow TEC" Atrium "
13 08.01.2013 08 2013 0 25 Moscow TEC" Atrium "
14 10.01.2013 10 2013 0 25 Moscow TEC" Atrium "
15 11.01.2013 11 2013 0 25 Moscow TEC" Atrium "
16 13.01.2013 13 2013 0 25 Moscow TEC" Atrium "
17 16.01.2013 16 2013 0 25 Moscow TEC" Atrium "
18 26.01.2013 26 2013 0 25 Moscow TEC" Atrium "
19 27.01.2013 27 2013 0 25 Moscow TEC" Atrium "
20 09.01.2013 09 2013 0 25 Moscow TEC" Atrium "
item_id item_name category_id \
0 22154 SCENE 2012 (BD) 37
1 2552 DEEP PURPLE The House Of Blue Light LP 58
3 2554 DEEP PURPLE Who Do You Think We Are LP 58
4 2555 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 2564 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 2565 DEEP PURPLE Stormbringer (firms). 56
7 2572 DEFTONES Koi No Yokan 55
8 2572 DEFTONES Koi No Yokan 55
9 2573 DEL REY LANA Born To Die 55
10 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 2593 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
month_name
0 January
1 January
3 January
4 January
5 January
6 January
7 January
8 January
9 January
10 January
11 January
12 January
13 January
14 January
15 January
16 January
17 January
18 January
19 January
20 January
(2928483, 14)
#removes month_num column
final_dataset.drop(columns=['month_num'], inplace=True)
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num year_num shop_id shop_name item_id \
0 02.01.2013 02 2013 59 Yaroslavl TC" Altair " 22154
1 03.01.2013 03 2013 25 Moscow TEC" Atrium " 2552
3 06.01.2013 06 2013 25 Moscow TEC" Atrium " 2554
4 15.01.2013 15 2013 25 Moscow TEC" Atrium " 2555
5 10.01.2013 10 2013 25 Moscow TEC" Atrium " 2564
6 02.01.2013 02 2013 25 Moscow TEC" Atrium " 2565
7 04.01.2013 04 2013 25 Moscow TEC" Atrium " 2572
8 11.01.2013 11 2013 25 Moscow TEC" Atrium " 2572
9 03.01.2013 03 2013 25 Moscow TEC" Atrium " 2573
10 03.01.2013 03 2013 25 Moscow TEC" Atrium " 2574
11 05.01.2013 05 2013 25 Moscow TEC" Atrium " 2574
12 07.01.2013 07 2013 25 Moscow TEC" Atrium " 2574
13 08.01.2013 08 2013 25 Moscow TEC" Atrium " 2574
14 10.01.2013 10 2013 25 Moscow TEC" Atrium " 2574
15 11.01.2013 11 2013 25 Moscow TEC" Atrium " 2574
16 13.01.2013 13 2013 25 Moscow TEC" Atrium " 2574
17 16.01.2013 16 2013 25 Moscow TEC" Atrium " 2574
18 26.01.2013 26 2013 25 Moscow TEC" Atrium " 2574
19 27.01.2013 27 2013 25 Moscow TEC" Atrium " 2574
20 09.01.2013 09 2013 25 Moscow TEC" Atrium " 2593
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
month_name
0 January
1 January
3 January
4 January
5 January
6 January
7 January
8 January
9 January
10 January
11 January
12 January
13 January
14 January
15 January
16 January
17 January
18 January
19 January
20 January
(2928483, 13)
#rearange the columns
final_dataset = final_dataset[['date', 'date_num', 'month_name', 'year_num', 'shop_id', 'shop_name', 'item_id', 'item_name', 'category_id', 'item_category_name', 'item_price', 'item_cnt_month', 'revenue']]
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id shop_name \
0 02.01.2013 02 January 2013 59 Yaroslavl TC" Altair "
1 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
3 06.01.2013 06 January 2013 25 Moscow TEC" Atrium "
4 15.01.2013 15 January 2013 25 Moscow TEC" Atrium "
5 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
6 02.01.2013 02 January 2013 25 Moscow TEC" Atrium "
7 04.01.2013 04 January 2013 25 Moscow TEC" Atrium "
8 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
9 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
10 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
11 05.01.2013 05 January 2013 25 Moscow TEC" Atrium "
12 07.01.2013 07 January 2013 25 Moscow TEC" Atrium "
13 08.01.2013 08 January 2013 25 Moscow TEC" Atrium "
14 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
15 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
16 13.01.2013 13 January 2013 25 Moscow TEC" Atrium "
17 16.01.2013 16 January 2013 25 Moscow TEC" Atrium "
18 26.01.2013 26 January 2013 25 Moscow TEC" Atrium "
19 27.01.2013 27 January 2013 25 Moscow TEC" Atrium "
20 09.01.2013 09 January 2013 25 Moscow TEC" Atrium "
item_id item_name category_id \
0 22154 SCENE 2012 (BD) 37
1 2552 DEEP PURPLE The House Of Blue Light LP 58
3 2554 DEEP PURPLE Who Do You Think We Are LP 58
4 2555 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 2564 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 2565 DEEP PURPLE Stormbringer (firms). 56
7 2572 DEFTONES Koi No Yokan 55
8 2572 DEFTONES Koi No Yokan 55
9 2573 DEL REY LANA Born To Die 55
10 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 2593 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
(2928483, 13)
#data binning
#found the bins using the following code
print(final_dataset['item_price'].max())
print(final_dataset['item_price'].min())
#creates a new column called price range
final_dataset['price_range'] = pd.cut(final_dataset['item_price'], bins=[-1, 100, 200, 300, 400, 500, 600, 700, 800, 900, 100000], labels=['0-100', '100-200', '200-300', '300-400', '400-500', '500-600', '600-700', '700-800', '800-900', '900-100000'])
59200.0 0.07
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id shop_name \
0 02.01.2013 02 January 2013 59 Yaroslavl TC" Altair "
1 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
3 06.01.2013 06 January 2013 25 Moscow TEC" Atrium "
4 15.01.2013 15 January 2013 25 Moscow TEC" Atrium "
5 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
6 02.01.2013 02 January 2013 25 Moscow TEC" Atrium "
7 04.01.2013 04 January 2013 25 Moscow TEC" Atrium "
8 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
9 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
10 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
11 05.01.2013 05 January 2013 25 Moscow TEC" Atrium "
12 07.01.2013 07 January 2013 25 Moscow TEC" Atrium "
13 08.01.2013 08 January 2013 25 Moscow TEC" Atrium "
14 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
15 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
16 13.01.2013 13 January 2013 25 Moscow TEC" Atrium "
17 16.01.2013 16 January 2013 25 Moscow TEC" Atrium "
18 26.01.2013 26 January 2013 25 Moscow TEC" Atrium "
19 27.01.2013 27 January 2013 25 Moscow TEC" Atrium "
20 09.01.2013 09 January 2013 25 Moscow TEC" Atrium "
item_id item_name category_id \
0 22154 SCENE 2012 (BD) 37
1 2552 DEEP PURPLE The House Of Blue Light LP 58
3 2554 DEEP PURPLE Who Do You Think We Are LP 58
4 2555 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 2564 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 2565 DEEP PURPLE Stormbringer (firms). 56
7 2572 DEFTONES Koi No Yokan 55
8 2572 DEFTONES Koi No Yokan 55
9 2573 DEL REY LANA Born To Die 55
10 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 2593 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range
0 900-100000
1 800-900
3 900-100000
4 900-100000
5 300-400
6 500-600
7 200-300
8 200-300
9 200-300
10 300-400
11 300-400
12 300-400
13 300-400
14 300-400
15 300-400
16 300-400
17 300-400
18 300-400
19 300-400
20 200-300
(2928483, 14)
#log transformation
#creates a new column called log_revenue
final_dataset['log_revenue'] = np.log(final_dataset['revenue'])
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id shop_name \
0 02.01.2013 02 January 2013 59 Yaroslavl TC" Altair "
1 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
3 06.01.2013 06 January 2013 25 Moscow TEC" Atrium "
4 15.01.2013 15 January 2013 25 Moscow TEC" Atrium "
5 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
6 02.01.2013 02 January 2013 25 Moscow TEC" Atrium "
7 04.01.2013 04 January 2013 25 Moscow TEC" Atrium "
8 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
9 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
10 03.01.2013 03 January 2013 25 Moscow TEC" Atrium "
11 05.01.2013 05 January 2013 25 Moscow TEC" Atrium "
12 07.01.2013 07 January 2013 25 Moscow TEC" Atrium "
13 08.01.2013 08 January 2013 25 Moscow TEC" Atrium "
14 10.01.2013 10 January 2013 25 Moscow TEC" Atrium "
15 11.01.2013 11 January 2013 25 Moscow TEC" Atrium "
16 13.01.2013 13 January 2013 25 Moscow TEC" Atrium "
17 16.01.2013 16 January 2013 25 Moscow TEC" Atrium "
18 26.01.2013 26 January 2013 25 Moscow TEC" Atrium "
19 27.01.2013 27 January 2013 25 Moscow TEC" Atrium "
20 09.01.2013 09 January 2013 25 Moscow TEC" Atrium "
item_id item_name category_id \
0 22154 SCENE 2012 (BD) 37
1 2552 DEEP PURPLE The House Of Blue Light LP 58
3 2554 DEEP PURPLE Who Do You Think We Are LP 58
4 2555 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 2564 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 2565 DEEP PURPLE Stormbringer (firms). 56
7 2572 DEFTONES Koi No Yokan 55
8 2572 DEFTONES Koi No Yokan 55
9 2573 DEL REY LANA Born To Die 55
10 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 2593 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range log_revenue
0 900-100000 6.906755
1 800-900 6.801283
3 900-100000 7.443693
4 900-100000 7.002156
5 300-400 5.855072
6 500-600 6.308098
7 200-300 5.476464
8 200-300 5.700444
9 200-300 6.799056
10 300-400 6.682109
11 300-400 5.988961
12 300-400 5.988961
13 300-400 6.682109
14 300-400 5.988961
15 300-400 6.682109
16 300-400 5.988961
17 300-400 5.988961
18 300-400 5.988961
19 300-400 5.988961
20 200-300 5.631212
(2928483, 15)
#encoding
#encodes the year_num column to 0, 1, 2
final_dataset['year_num'] = final_dataset['year_num'].replace({'2013': 0, '2014': 1, '2015': 2})
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id \
0 02.01.2013 02 January 0 59
1 03.01.2013 03 January 0 25
3 06.01.2013 06 January 0 25
4 15.01.2013 15 January 0 25
5 10.01.2013 10 January 0 25
6 02.01.2013 02 January 0 25
7 04.01.2013 04 January 0 25
8 11.01.2013 11 January 0 25
9 03.01.2013 03 January 0 25
10 03.01.2013 03 January 0 25
11 05.01.2013 05 January 0 25
12 07.01.2013 07 January 0 25
13 08.01.2013 08 January 0 25
14 10.01.2013 10 January 0 25
15 11.01.2013 11 January 0 25
16 13.01.2013 13 January 0 25
17 16.01.2013 16 January 0 25
18 26.01.2013 26 January 0 25
19 27.01.2013 27 January 0 25
20 09.01.2013 09 January 0 25
shop_name item_id \
0 Yaroslavl TC" Altair " 22154
1 Moscow TEC" Atrium " 2552
3 Moscow TEC" Atrium " 2554
4 Moscow TEC" Atrium " 2555
5 Moscow TEC" Atrium " 2564
6 Moscow TEC" Atrium " 2565
7 Moscow TEC" Atrium " 2572
8 Moscow TEC" Atrium " 2572
9 Moscow TEC" Atrium " 2573
10 Moscow TEC" Atrium " 2574
11 Moscow TEC" Atrium " 2574
12 Moscow TEC" Atrium " 2574
13 Moscow TEC" Atrium " 2574
14 Moscow TEC" Atrium " 2574
15 Moscow TEC" Atrium " 2574
16 Moscow TEC" Atrium " 2574
17 Moscow TEC" Atrium " 2574
18 Moscow TEC" Atrium " 2574
19 Moscow TEC" Atrium " 2574
20 Moscow TEC" Atrium " 2593
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range log_revenue
0 900-100000 6.906755
1 800-900 6.801283
3 900-100000 7.443693
4 900-100000 7.002156
5 300-400 5.855072
6 500-600 6.308098
7 200-300 5.476464
8 200-300 5.700444
9 200-300 6.799056
10 300-400 6.682109
11 300-400 5.988961
12 300-400 5.988961
13 300-400 6.682109
14 300-400 5.988961
15 300-400 6.682109
16 300-400 5.988961
17 300-400 5.988961
18 300-400 5.988961
19 300-400 5.988961
20 200-300 5.631212
(2928483, 15)
#grouping and aggregation
#grouping the data set by shop_id and year_num and aggregating the item_cnt_month column using sum
grouped_by_shop_id_and_year_num = final_dataset.groupby(['shop_id', 'year_num']).agg({'item_cnt_month': 'sum'})
print("\n\nHead of grouped_by_shop_id_and_year_num:\n")
print(grouped_by_shop_id_and_year_num.head(60))
print(grouped_by_shop_id_and_year_num.shape)
Head of grouped_by_shop_id_and_year_num:
item_cnt_month
shop_id year_num
0 0 11705
1 0 6311
2 0 9989
1 12247
2 8470
3 0 10242
1 11039
2 7194
4 0 19054
1 15909
2 9106
5 0 14717
1 17041
2 11089
6 0 46707
1 35496
2 18554
7 0 28218
1 24530
2 14411
8 0 3602
9 0 6531
1 6155
2 3188
10 0 11132
1 8859
2 4532
11 2 572
12 0 19494
1 26379
2 24947
13 0 13529
1 6234
14 0 20114
1 16003
2 10397
15 0 32339
1 24854
2 14138
16 0 30250
1 19569
2 11953
17 0 9913
1 15308
2 697
18 0 33632
1 18815
2 13174
19 0 32860
1 25353
2 15455
20 1 3262
2 2611
21 0 25258
1 25167
2 18259
22 0 26943
1 20341
2 13050
23 0 7722
(159, 1)
#creates a new column called scaled_revenue
final_dataset['scaled_revenue'] = (final_dataset['revenue'] - final_dataset['revenue'].min()) / (final_dataset['revenue'].max() - final_dataset['revenue'].min())
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id \
0 02.01.2013 02 January 0 59
1 03.01.2013 03 January 0 25
3 06.01.2013 06 January 0 25
4 15.01.2013 15 January 0 25
5 10.01.2013 10 January 0 25
6 02.01.2013 02 January 0 25
7 04.01.2013 04 January 0 25
8 11.01.2013 11 January 0 25
9 03.01.2013 03 January 0 25
10 03.01.2013 03 January 0 25
11 05.01.2013 05 January 0 25
12 07.01.2013 07 January 0 25
13 08.01.2013 08 January 0 25
14 10.01.2013 10 January 0 25
15 11.01.2013 11 January 0 25
16 13.01.2013 13 January 0 25
17 16.01.2013 16 January 0 25
18 26.01.2013 26 January 0 25
19 27.01.2013 27 January 0 25
20 09.01.2013 09 January 0 25
shop_name item_id \
0 Yaroslavl TC" Altair " 22154
1 Moscow TEC" Atrium " 2552
3 Moscow TEC" Atrium " 2554
4 Moscow TEC" Atrium " 2555
5 Moscow TEC" Atrium " 2564
6 Moscow TEC" Atrium " 2565
7 Moscow TEC" Atrium " 2572
8 Moscow TEC" Atrium " 2572
9 Moscow TEC" Atrium " 2573
10 Moscow TEC" Atrium " 2574
11 Moscow TEC" Atrium " 2574
12 Moscow TEC" Atrium " 2574
13 Moscow TEC" Atrium " 2574
14 Moscow TEC" Atrium " 2574
15 Moscow TEC" Atrium " 2574
16 Moscow TEC" Atrium " 2574
17 Moscow TEC" Atrium " 2574
18 Moscow TEC" Atrium " 2574
19 Moscow TEC" Atrium " 2574
20 Moscow TEC" Atrium " 2593
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range log_revenue scaled_revenue
0 900-100000 6.906755 0.000546
1 800-900 6.801283 0.000491
3 900-100000 7.443693 0.000934
4 900-100000 7.002156 0.000601
5 300-400 5.855072 0.000191
6 500-600 6.308098 0.000300
7 200-300 5.476464 0.000131
8 200-300 5.700444 0.000163
9 200-300 6.799056 0.000490
10 300-400 6.682109 0.000436
11 300-400 5.988961 0.000218
12 300-400 5.988961 0.000218
13 300-400 6.682109 0.000436
14 300-400 5.988961 0.000218
15 300-400 6.682109 0.000436
16 300-400 5.988961 0.000218
17 300-400 5.988961 0.000218
18 300-400 5.988961 0.000218
19 300-400 5.988961 0.000218
20 200-300 5.631212 0.000152
(2928483, 16)
#change month_name column to numeric
final_dataset['month_name'] = final_dataset['month_name'].replace({'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June':6, 'July': 7, 'August': 8, 'September': 9, 'October': 10, 'November':11, 'December': 12})
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
Head of final_dataset:
date date_num month_name year_num shop_id \
0 02.01.2013 02 1 0 59
1 03.01.2013 03 1 0 25
3 06.01.2013 06 1 0 25
4 15.01.2013 15 1 0 25
5 10.01.2013 10 1 0 25
6 02.01.2013 02 1 0 25
7 04.01.2013 04 1 0 25
8 11.01.2013 11 1 0 25
9 03.01.2013 03 1 0 25
10 03.01.2013 03 1 0 25
11 05.01.2013 05 1 0 25
12 07.01.2013 07 1 0 25
13 08.01.2013 08 1 0 25
14 10.01.2013 10 1 0 25
15 11.01.2013 11 1 0 25
16 13.01.2013 13 1 0 25
17 16.01.2013 16 1 0 25
18 26.01.2013 26 1 0 25
19 27.01.2013 27 1 0 25
20 09.01.2013 09 1 0 25
shop_name item_id \
0 Yaroslavl TC" Altair " 22154
1 Moscow TEC" Atrium " 2552
3 Moscow TEC" Atrium " 2554
4 Moscow TEC" Atrium " 2555
5 Moscow TEC" Atrium " 2564
6 Moscow TEC" Atrium " 2565
7 Moscow TEC" Atrium " 2572
8 Moscow TEC" Atrium " 2572
9 Moscow TEC" Atrium " 2573
10 Moscow TEC" Atrium " 2574
11 Moscow TEC" Atrium " 2574
12 Moscow TEC" Atrium " 2574
13 Moscow TEC" Atrium " 2574
14 Moscow TEC" Atrium " 2574
15 Moscow TEC" Atrium " 2574
16 Moscow TEC" Atrium " 2574
17 Moscow TEC" Atrium " 2574
18 Moscow TEC" Atrium " 2574
19 Moscow TEC" Atrium " 2574
20 Moscow TEC" Atrium " 2593
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range log_revenue scaled_revenue
0 900-100000 6.906755 0.000546
1 800-900 6.801283 0.000491
3 900-100000 7.443693 0.000934
4 900-100000 7.002156 0.000601
5 300-400 5.855072 0.000191
6 500-600 6.308098 0.000300
7 200-300 5.476464 0.000131
8 200-300 5.700444 0.000163
9 200-300 6.799056 0.000490
10 300-400 6.682109 0.000436
11 300-400 5.988961 0.000218
12 300-400 5.988961 0.000218
13 300-400 6.682109 0.000436
14 300-400 5.988961 0.000218
15 300-400 6.682109 0.000436
16 300-400 5.988961 0.000218
17 300-400 5.988961 0.000218
18 300-400 5.988961 0.000218
19 300-400 5.988961 0.000218
20 200-300 5.631212 0.000152
(2928483, 16)
#correlation
numeric_columns = final_dataset.select_dtypes(include=['number'])
print("\n\nCorrelation of final_dataset:\n")
print(numeric_columns.corr())
Correlation of final_dataset:
month_name year_num shop_id item_id category_id \
month_name 1.000000 -0.134131 0.020646 -0.002103 -0.001055
year_num -0.134131 1.000000 0.011835 0.010486 0.030406
shop_id 0.020646 0.011835 1.000000 0.029344 0.019339
item_id -0.002103 0.010486 0.029344 1.000000 0.369384
category_id -0.001055 0.030406 0.019339 0.369384 1.000000
item_price 0.044361 0.080574 -0.023966 -0.134759 -0.255033
item_cnt_month 0.021007 0.002510 -0.005660 0.018896 0.016922
revenue 0.030326 0.029541 -0.013540 -0.063423 -0.117860
log_revenue 0.057248 0.096145 -0.039415 -0.310144 -0.342896
scaled_revenue 0.030326 0.029541 -0.013540 -0.063423 -0.117860
item_price item_cnt_month revenue log_revenue \
month_name 0.044361 0.021007 0.030326 0.057248
year_num 0.080574 0.002510 0.029541 0.096145
shop_id -0.023966 -0.005660 -0.013540 -0.039415
item_id -0.134759 0.018896 -0.063423 -0.310144
category_id -0.255033 0.016922 -0.117860 -0.342896
item_price 1.000000 0.014437 0.436314 0.601371
item_cnt_month 0.014437 1.000000 0.481568 0.115253
revenue 0.436314 0.481568 1.000000 0.331392
log_revenue 0.601371 0.115253 0.331392 1.000000
scaled_revenue 0.436314 0.481568 1.000000 0.331392
scaled_revenue
month_name 0.030326
year_num 0.029541
shop_id -0.013540
item_id -0.063423
category_id -0.117860
item_price 0.436314
item_cnt_month 0.481568
revenue 1.000000
log_revenue 0.331392
scaled_revenue 1.000000
#checks for missing values
print("\n\nMissing values in final_dataset:\n")
print(final_dataset.isnull().sum())
#checks for null values
print("\n\nNull values in final_dataset:\n")
print(final_dataset.isnull().sum())
Missing values in final_dataset: date 0 date_num 0 month_name 0 year_num 0 shop_id 0 shop_name 0 item_id 0 item_name 0 category_id 0 item_category_name 0 item_price 0 item_cnt_month 0 revenue 0 price_range 0 log_revenue 0 scaled_revenue 0 dtype: int64 Null values in final_dataset: date 0 date_num 0 month_name 0 year_num 0 shop_id 0 shop_name 0 item_id 0 item_name 0 category_id 0 item_category_name 0 item_price 0 item_cnt_month 0 revenue 0 price_range 0 log_revenue 0 scaled_revenue 0 dtype: int64
#Descriptive analytics
# Summary Statistics
print("\nDescriptive statistics of final_dataset:")
print(final_dataset.describe())
Descriptive statistics of final_dataset:
month_name year_num shop_id item_id category_id \
count 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06
mean 6.248408e+00 7.767790e-01 3.300296e+01 1.020028e+04 4.001637e+01
std 3.535921e+00 7.684598e-01 1.622543e+01 6.324391e+03 1.709809e+01
min 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
25% 3.000000e+00 0.000000e+00 2.200000e+01 4.477000e+03 2.800000e+01
50% 6.000000e+00 1.000000e+00 3.100000e+01 9.355000e+03 4.000000e+01
75% 9.000000e+00 1.000000e+00 4.700000e+01 1.569100e+04 5.500000e+01
max 1.200000e+01 2.000000e+00 5.900000e+01 2.216900e+04 8.300000e+01
item_price item_cnt_month revenue log_revenue \
count 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06
mean 8.893627e+02 1.247257e+00 1.164267e+03 6.254676e+00
std 1.718155e+03 2.217429e+00 5.684853e+03 1.171779e+00
min 7.000000e-02 1.000000e+00 7.000000e-02 -2.659260e+00
25% 2.490000e+02 1.000000e+00 2.490000e+02 5.517453e+00
50% 3.990000e+02 1.000000e+00 4.490000e+02 6.107023e+00
75% 9.990000e+02 1.000000e+00 1.090000e+03 6.993933e+00
max 5.920000e+04 6.690000e+02 1.829990e+06 1.441982e+01
scaled_revenue
count 2.928483e+06
mean 6.361770e-04
std 3.106494e-03
min 0.000000e+00
25% 1.360281e-04
50% 2.453183e-04
75% 5.955934e-04
max 1.000000e+00
#seasonality analysis
grouped_by_month_name = final_dataset.groupby(['month_name']).agg({'item_cnt_month': 'sum'})
print("\n\nHead of grouped_by_month_name:\n")
print(grouped_by_month_name)
print(grouped_by_month_name.shape)
Head of grouped_by_month_name:
item_cnt_month
month_name
1 359391
2 322775
3 345171
4 282131
5 277519
6 287443
7 271985
8 294692
9 305967
10 304453
11 248320
12 352723
(12, 1)
#performing seasonal decomposition
decomposition = sm.tsa.seasonal_decompose(grouped_by_month_name, model='additive', period=1)
#plotting the seasonal decomposition
fig = decomposition.plot()
plt.show()
#plotting the item_cnt_month column
plt.figure(figsize=(20, 10))
plt.plot(final_dataset['item_cnt_month'])
plt.title('Item Count Per Month')
plt.xlabel('Month')
plt.ylabel('Item Count')
plt.show()
#regulatory analytics
grouped_by_shop_id_and_year_num = final_dataset.groupby(['shop_id', 'year_num']).agg({'item_cnt_month': 'sum'})
print("\n\nHead of grouped_by_shop_id_and_year_num:\n")
print(grouped_by_shop_id_and_year_num.head(60))
Head of grouped_by_shop_id_and_year_num:
item_cnt_month
shop_id year_num
0 0 11705
1 0 6311
2 0 9989
1 12247
2 8470
3 0 10242
1 11039
2 7194
4 0 19054
1 15909
2 9106
5 0 14717
1 17041
2 11089
6 0 46707
1 35496
2 18554
7 0 28218
1 24530
2 14411
8 0 3602
9 0 6531
1 6155
2 3188
10 0 11132
1 8859
2 4532
11 2 572
12 0 19494
1 26379
2 24947
13 0 13529
1 6234
14 0 20114
1 16003
2 10397
15 0 32339
1 24854
2 14138
16 0 30250
1 19569
2 11953
17 0 9913
1 15308
2 697
18 0 33632
1 18815
2 13174
19 0 32860
1 25353
2 15455
20 1 3262
2 2611
21 0 25258
1 25167
2 18259
22 0 26943
1 20341
2 13050
23 0 7722
#Variable Identification
# Identify numerical and categorical variables
numerical_vars = final_dataset.select_dtypes(include=['int64', 'float64']).columns
categorical_vars = final_dataset.select_dtypes(include=['object', 'category']).columns
# Print the list of numerical and categorical variables
print("Numerical Variables:")
print(numerical_vars)
print("\nCategorical Variables:")
print(categorical_vars)
Numerical Variables:
Index(['month_name', 'year_num', 'shop_id', 'item_id', 'category_id',
'item_price', 'item_cnt_month', 'revenue', 'log_revenue',
'scaled_revenue'],
dtype='object')
Categorical Variables:
Index(['date', 'date_num', 'shop_name', 'item_name', 'item_category_name',
'price_range'],
dtype='object')
# univariate analysis
for column in final_dataset.columns:
variable_type = final_dataset[column].dtype
summary_stats = final_dataset[column].describe()
plt.figure(figsize=(10, 6))
# For numerical variables, create a histogram
if variable_type in ['int64', 'float64']:
sns.histplot(data=final_dataset, x=column, kde=True)
plt.title(f'Distribution of {column}')
plt.xlabel(column)
plt.ylabel('Frequency')
# For categorical variables, create a bar plot
else:
sns.countplot(data=final_dataset, x=column)
plt.title(f'Counts of {column}')
plt.xlabel(column)
plt.ylabel('Count')
plt.show()
# Print summary statistics
print(f"Summary Statistics for {column}:")
print(summary_stats)
Summary Statistics for date: count 2928483 unique 1034 top 28.12.2013 freq 9415 Name: date, dtype: object
Summary Statistics for date_num: count 2928483 unique 31 top 02 freq 103081 Name: date_num, dtype: object
Summary Statistics for month_name: count 2.928483e+06 mean 6.248408e+00 std 3.535921e+00 min 1.000000e+00 25% 3.000000e+00 50% 6.000000e+00 75% 9.000000e+00 max 1.200000e+01 Name: month_name, dtype: float64
Summary Statistics for year_num: count 2.928483e+06 mean 7.767790e-01 std 7.684598e-01 min 0.000000e+00 25% 0.000000e+00 50% 1.000000e+00 75% 1.000000e+00 max 2.000000e+00 Name: year_num, dtype: float64
Summary Statistics for shop_id: count 2.928483e+06 mean 3.300296e+01 std 1.622543e+01 min 0.000000e+00 25% 2.200000e+01 50% 3.100000e+01 75% 4.700000e+01 max 5.900000e+01 Name: shop_id, dtype: float64
Summary Statistics for shop_name: count 2928483 unique 60 top Moscow TC" Semenovsky " freq 235185 Name: shop_name, dtype: object
Summary Statistics for item_id: count 2.928483e+06 mean 1.020028e+04 std 6.324391e+03 min 0.000000e+00 25% 4.477000e+03 50% 9.355000e+03 75% 1.569100e+04 max 2.216900e+04 Name: item_id, dtype: float64
Summary Statistics for item_name: count 2928483 unique 21782 top Corporate package T-shirt 1C Interest white (3... freq 31336 Name: item_name, dtype: object
Summary Statistics for category_id: count 2.928483e+06 mean 4.001637e+01 std 1.709809e+01 min 0.000000e+00 25% 2.800000e+01 50% 4.000000e+01 75% 5.500000e+01 max 8.300000e+01 Name: category_id, dtype: float64
Summary Statistics for item_category_name: count 2928483 unique 84 top Cinema - DVD freq 563937 Name: item_category_name, dtype: object
Summary Statistics for item_price: count 2.928483e+06 mean 8.893627e+02 std 1.718155e+03 min 7.000000e-02 25% 2.490000e+02 50% 3.990000e+02 75% 9.990000e+02 max 5.920000e+04 Name: item_price, dtype: float64
Summary Statistics for item_cnt_month: count 2.928483e+06 mean 1.247257e+00 std 2.217429e+00 min 1.000000e+00 25% 1.000000e+00 50% 1.000000e+00 75% 1.000000e+00 max 6.690000e+02 Name: item_cnt_month, dtype: float64
Summary Statistics for revenue: count 2.928483e+06 mean 1.164267e+03 std 5.684853e+03 min 7.000000e-02 25% 2.490000e+02 50% 4.490000e+02 75% 1.090000e+03 max 1.829990e+06 Name: revenue, dtype: float64
Summary Statistics for price_range: count 2928483 unique 10 top 900-100000 freq 772112 Name: price_range, dtype: object
Summary Statistics for log_revenue: count 2.928483e+06 mean 6.254676e+00 std 1.171779e+00 min -2.659260e+00 25% 5.517453e+00 50% 6.107023e+00 75% 6.993933e+00 max 1.441982e+01 Name: log_revenue, dtype: float64
Summary Statistics for scaled_revenue: count 2.928483e+06 mean 6.361770e-04 std 3.106494e-03 min 0.000000e+00 25% 1.360281e-04 50% 2.453183e-04 75% 5.955934e-04 max 1.000000e+00 Name: scaled_revenue, dtype: float64
#bivariate analysis
#can analysis by changing var1 and var2
var1 = 'item_price'
var2 = 'item_cnt_month'
var1_type = final_dataset[var1].dtype
var2_type = final_dataset[var2].dtype
# Scatter Plot for Numerical vs. Numerical
if var1_type in ['int64', 'float64'] and var2_type in ['int64', 'float64']:
plt.figure(figsize=(10, 6))
sns.scatterplot(data=final_dataset, x=var1, y=var2)
plt.title(f'Scatter Plot: {var1} vs. {var2}')
plt.xlabel(var1)
plt.ylabel(var2)
plt.grid(True)
plt.show()
# Box Plot for Categorical vs. Numerical
elif var1_type in ['object', 'category'] and var2_type in ['int64', 'float64']:
plt.figure(figsize=(10, 6))
sns.boxplot(data=final_dataset, x=var1, y=var2)
plt.title(f'Box Plot: {var1} vs. {var2}')
plt.xlabel(var1)
plt.ylabel(var2)
plt.grid(True)
plt.show()
# Bar Plot for Categorical vs. Categorical
elif var1_type in ['object', 'category'] and var2_type in ['object', 'category']:
crosstab = pd.crosstab(final_dataset[var1], final_dataset[var2])
crosstab.plot(kind='bar', stacked=True, figsize=(10, 6))
plt.title(f'Bar Plot: {var1} vs. {var2}')
plt.xlabel(var1)
plt.ylabel('Count')
plt.grid(True)
plt.show()
# Print correlation for Numerical vs. Numerical
if var1_type in ['int64', 'float64'] and var2_type in ['int64', 'float64']:
correlation = final_dataset[[var1, var2]].corr().iloc[0, 1]
print(f'Correlation between {var1} and {var2}: {correlation:.2f}')
Correlation between item_price and item_cnt_month: 0.01
#Exploratory Data Analysis (EDA)
print("Dataset Overview:")
print(final_dataset.info())
print("\nSummary Statistics for Numerical Variables:")
print(final_dataset.describe())
print("\nMissing Values:")
print(final_dataset.isnull().sum())
numerical_columns = ['month_name', 'year_num', 'shop_id', 'item_id', 'category_id', 'item_price', 'item_cnt_month', 'revenue', 'log_revenue', 'scaled_revenue']
for column in numerical_columns:
plt.figure(figsize=(8, 4))
sns.histplot(data=final_dataset, x=column, kde=True, bins=20)
plt.title(f'Distribution of {column}')
plt.xlabel(column)
plt.ylabel('Frequency')
plt.show()
# Visualize relationships between variables with a correlation matrix for numerical variables
correlation_matrix = final_dataset[numerical_columns].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title("Correlation Heatmap for Numerical Variables")
plt.show()
# Explore categorical variables with bar plots
categorical_columns = ['shop_name', 'item_name', 'item_category_name', 'price_range']
for column in categorical_columns:
plt.figure(figsize=(10, 6))
sns.countplot(data=final_dataset, x=column)
plt.title(f'Counts of {column}')
plt.xlabel(column)
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()
Dataset Overview:
<class 'pandas.core.frame.DataFrame'>
Index: 2928483 entries, 0 to 2935848
Data columns (total 16 columns):
# Column Dtype
--- ------ -----
0 date object
1 date_num object
2 month_name int64
3 year_num int64
4 shop_id int64
5 shop_name object
6 item_id int64
7 item_name object
8 category_id int64
9 item_category_name object
10 item_price float64
11 item_cnt_month int64
12 revenue float64
13 price_range category
14 log_revenue float64
15 scaled_revenue float64
dtypes: category(1), float64(4), int64(6), object(5)
memory usage: 360.3+ MB
None
Summary Statistics for Numerical Variables:
month_name year_num shop_id item_id category_id \
count 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06
mean 6.248408e+00 7.767790e-01 3.300296e+01 1.020028e+04 4.001637e+01
std 3.535921e+00 7.684598e-01 1.622543e+01 6.324391e+03 1.709809e+01
min 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
25% 3.000000e+00 0.000000e+00 2.200000e+01 4.477000e+03 2.800000e+01
50% 6.000000e+00 1.000000e+00 3.100000e+01 9.355000e+03 4.000000e+01
75% 9.000000e+00 1.000000e+00 4.700000e+01 1.569100e+04 5.500000e+01
max 1.200000e+01 2.000000e+00 5.900000e+01 2.216900e+04 8.300000e+01
item_price item_cnt_month revenue log_revenue \
count 2.928483e+06 2.928483e+06 2.928483e+06 2.928483e+06
mean 8.893627e+02 1.247257e+00 1.164267e+03 6.254676e+00
std 1.718155e+03 2.217429e+00 5.684853e+03 1.171779e+00
min 7.000000e-02 1.000000e+00 7.000000e-02 -2.659260e+00
25% 2.490000e+02 1.000000e+00 2.490000e+02 5.517453e+00
50% 3.990000e+02 1.000000e+00 4.490000e+02 6.107023e+00
75% 9.990000e+02 1.000000e+00 1.090000e+03 6.993933e+00
max 5.920000e+04 6.690000e+02 1.829990e+06 1.441982e+01
scaled_revenue
count 2.928483e+06
mean 6.361770e-04
std 3.106494e-03
min 0.000000e+00
25% 1.360281e-04
50% 2.453183e-04
75% 5.955934e-04
max 1.000000e+00
Missing Values:
date 0
date_num 0
month_name 0
year_num 0
shop_id 0
shop_name 0
item_id 0
item_name 0
category_id 0
item_category_name 0
item_price 0
item_cnt_month 0
revenue 0
price_range 0
log_revenue 0
scaled_revenue 0
dtype: int64
#inferential analysis
np.random.seed(42)
data = np.random.normal(loc=70, scale=10, size=100)
# Create a DataFrame from the generated data
df = pd.DataFrame({'measurement': data})
# Calculate the sample mean and standard deviation
sample_mean = df['measurement'].mean()
sample_std = df['measurement'].std()
# Define a hypothetical population mean for comparison
population_mean = 75
# Perform a t-test to compare the sample mean with the population mean
t_statistic, p_value = stats.ttest_1samp(df['measurement'], population_mean)
# Print results
print(f"Sample Mean: {sample_mean:.2f}")
print(f"Sample Standard Deviation: {sample_std:.2f}")
print(f"Population Mean: {population_mean}")
print(f"T-Statistic: {t_statistic:.2f}")
print(f"P-Value: {p_value:.4f}")
# Determine statistical significance
alpha = 0.05 # Significance level (adjust as needed)
if p_value < alpha:
print("Reject the null hypothesis: The sample mean is statistically different from the population mean.")
else:
print("Fail to reject the null hypothesis: There is no significant difference between the sample mean and the population mean.")
Sample Mean: 68.96 Sample Standard Deviation: 9.08 Population Mean: 75 T-Statistic: -6.65 P-Value: 0.0000 Reject the null hypothesis: The sample mean is statistically different from the population mean.
#dianostic analytics
# Generate a hypothetical dataset
np.random.seed(42)
X = np.random.rand(100, 1) * 10
y = 3 * X + 2 + np.random.randn(100, 1)
# Create a DataFrame from the generated data
df = pd.DataFrame({'X': X.flatten(), 'y': y.flatten()})
# Diagnostic Plots
plt.figure(figsize=(12, 6))
plt.tight_layout()
plt.show()
<Figure size 1200x600 with 0 Axes>
#qualitative analytics
category_counts = final_dataset['item_category_name'].value_counts()
print(category_counts)
cross_tab = pd.crosstab(final_dataset['shop_name'], final_dataset['item_category_name'])
print(cross_tab)
category_frequency = (final_dataset['price_range'] == 'Low').sum()
print(f"Frequency of 'Low' price range: {category_frequency}")
average_price_per_category = final_dataset.groupby('item_category_name')['item_price'].mean()
print(average_price_per_category)
category_counts.plot(kind='bar', figsize=(10, 6))
plt.title('Item Category Counts')
plt.xlabel('Category')
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()
item_category_name
Cinema - DVD 563937
PC Games - Standard Editions 350787
Music - CD of local production 339127
Games - PS3 207371
Cinema - Blu-Ray 191931
...
Books - Guides 3
Accessories - PS2 2
Books - Postcards 2
Books - Cognitive literature 1
Game consoles - PS2 1
Name: count, Length: 84, dtype: int64
item_category_name Accessories - PS2 \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 0
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 1
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 1
! Yakutsk TC "Central" fran 0
item_category_name Accessories - PS3 \
shop_name
Adygea TC "Mega" 338
Balashikha TC "Oktyabr-Kinomir" 199
Chekhov TC" Karnaval " 319
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 213
Kaluga TC "XXI century" 337
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 218
Khimki TC" Mega " 814
Kolomna TC "Rio" 418
Krasnoyarsk TC "June" 689
Krasnoyarsk TC "Vzletka Plaza" 191
Kursk TC " Pushkinskiy " 424
Moscow MTRC" Afi Mall " 244
Moscow Shop C21 119
Moscow TC" MEGA Belaya Dacha II " 868
Moscow TC" MEGA Teply Stan "II 986
Moscow TC" New Age "(Novokosino) 461
Moscow TC" Perlovsky " 360
Moscow TC" Semenovsky " 1154
Moscow TC" Serebryany House " 38
Moscow TEC" Atrium " 972
Moscow TK" Budenovskiy "(pav.A2) 71
Moscow TK" Budenovskiy "(pav.K7) 368
Moscow TTS" Areal "(Belyaevo) 307
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 41
N.Novgorod SEC" Fantasy " 382
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 226
Omsk TC" Mega " 485
Outbound Trade 22
RostovNaDon Mega shopping center 309
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 46
Samara Melody shopping center 128
Samara ParkHouse shopping center 210
Sergiev Posad 7Ya shopping center 472
St. Petersburg Nevsky Center shopping center 548
St. Petersburg Sennaya shopping center 354
Surgut SEC "City Mall" 552
Tomsk SEC "Emerald City" 104
Tyumen SEC "Crystal" 102
Tyumen SEC "Goodwin" 665
Tyumen TC "Green Coast " 222
Ufa TC" Central " 244
Ufa TC" Family "2 329
Volga TC "Volga Mall" 301
Vologda SEC "Marmelad" 229
Voronezh (Plekhanovskaya, 13) 414
Voronezh SEC "Maksimir" 447
Voronezh SEC City-Park "Grad" 19
Yakutsk Ordzhonikidze, 56 592
Yakutsk TC" Central " 308
Yaroslavl TC" Altair " 218
Zhukovsky st. Chkalov 39m? 134
Zhukovsky st. Chkalov 39m² 2
! Yakutsk Ordzhonikidze, 56 francs 93
! Yakutsk TC "Central" fran 58
item_category_name Accessories - PS4 \
shop_name
Adygea TC "Mega" 470
Balashikha TC "Oktyabr-Kinomir" 344
Chekhov TC" Karnaval " 425
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 514
Kaluga TC "XXI century" 460
Kazan TC "Behetle" 8
Kazan TC "ParkHouse" II 389
Khimki TC" Mega " 868
Kolomna TC "Rio" 469
Krasnoyarsk TC "June" 636
Krasnoyarsk TC "Vzletka Plaza" 218
Kursk TC " Pushkinskiy " 457
Moscow MTRC" Afi Mall " 581
Moscow Shop C21 322
Moscow TC" MEGA Belaya Dacha II " 1076
Moscow TC" MEGA Teply Stan "II 1229
Moscow TC" New Age "(Novokosino) 514
Moscow TC" Perlovsky " 373
Moscow TC" Semenovsky " 1614
Moscow TC" Serebryany House " 13
Moscow TEC" Atrium " 1539
Moscow TK" Budenovskiy "(pav.A2) 11
Moscow TK" Budenovskiy "(pav.K7) 496
Moscow TTS" Areal "(Belyaevo) 492
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 66
N.Novgorod SEC" Fantasy " 563
N.Novgorod SEC" RIO " 83
Novosibirsk SEC" Gallery Novosibirsk " 6
Novosibirsk SEC" Mega " 357
Omsk TC" Mega " 532
Outbound Trade 34
RostovNaDon Mega shopping center 295
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 1
RostovNaDonu SEC" Megacenter Horizon 141
Samara Melody shopping center 231
Samara ParkHouse shopping center 267
Sergiev Posad 7Ya shopping center 645
St. Petersburg Nevsky Center shopping center 1117
St. Petersburg Sennaya shopping center 487
Surgut SEC "City Mall" 862
Tomsk SEC "Emerald City" 355
Tyumen SEC "Crystal" 239
Tyumen SEC "Goodwin" 638
Tyumen TC "Green Coast " 237
Ufa TC" Central " 358
Ufa TC" Family "2 426
Volga TC "Volga Mall" 317
Vologda SEC "Marmelad" 313
Voronezh (Plekhanovskaya, 13) 644
Voronezh SEC "Maksimir" 508
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 786
Yakutsk TC" Central " 512
Yaroslavl TC" Altair " 370
Zhukovsky st. Chkalov 39m? 196
Zhukovsky st. Chkalov 39m² 4
! Yakutsk Ordzhonikidze, 56 francs 35
! Yakutsk TC "Central" fran 15
item_category_name Accessories - PSP \
shop_name
Adygea TC "Mega" 39
Balashikha TC "Oktyabr-Kinomir" 28
Chekhov TC" Karnaval " 45
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 9
Kaluga TC "XXI century" 62
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 19
Khimki TC" Mega " 98
Kolomna TC "Rio" 136
Krasnoyarsk TC "June" 49
Krasnoyarsk TC "Vzletka Plaza" 17
Kursk TC " Pushkinskiy " 59
Moscow MTRC" Afi Mall " 17
Moscow Shop C21 11
Moscow TC" MEGA Belaya Dacha II " 95
Moscow TC" MEGA Teply Stan "II 61
Moscow TC" New Age "(Novokosino) 52
Moscow TC" Perlovsky " 68
Moscow TC" Semenovsky " 136
Moscow TC" Serebryany House " 9
Moscow TEC" Atrium " 45
Moscow TK" Budenovskiy "(pav.A2) 4
Moscow TK" Budenovskiy "(pav.K7) 38
Moscow TTS" Areal "(Belyaevo) 21
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 1
N.Novgorod SEC" Fantasy " 19
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 19
Omsk TC" Mega " 38
Outbound Trade 0
RostovNaDon Mega shopping center 44
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 2
Samara Melody shopping center 4
Samara ParkHouse shopping center 23
Sergiev Posad 7Ya shopping center 41
St. Petersburg Nevsky Center shopping center 35
St. Petersburg Sennaya shopping center 36
Surgut SEC "City Mall" 85
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 12
Tyumen SEC "Goodwin" 90
Tyumen TC "Green Coast " 55
Ufa TC" Central " 55
Ufa TC" Family "2 68
Volga TC "Volga Mall" 90
Vologda SEC "Marmelad" 39
Voronezh (Plekhanovskaya, 13) 22
Voronezh SEC "Maksimir" 90
Voronezh SEC City-Park "Grad" 2
Yakutsk Ordzhonikidze, 56 115
Yakutsk TC" Central " 73
Yaroslavl TC" Altair " 52
Zhukovsky st. Chkalov 39m? 6
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 35
! Yakutsk TC "Central" fran 23
item_category_name Accessories - PSVita \
shop_name
Adygea TC "Mega" 49
Balashikha TC "Oktyabr-Kinomir" 107
Chekhov TC" Karnaval " 153
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 100
Kaluga TC "XXI century" 66
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 48
Khimki TC" Mega " 275
Kolomna TC "Rio" 176
Krasnoyarsk TC "June" 208
Krasnoyarsk TC "Vzletka Plaza" 64
Kursk TC " Pushkinskiy " 136
Moscow MTRC" Afi Mall " 169
Moscow Shop C21 115
Moscow TC" MEGA Belaya Dacha II " 403
Moscow TC" MEGA Teply Stan "II 306
Moscow TC" New Age "(Novokosino) 168
Moscow TC" Perlovsky " 167
Moscow TC" Semenovsky " 508
Moscow TC" Serebryany House " 21
Moscow TEC" Atrium " 449
Moscow TK" Budenovskiy "(pav.A2) 20
Moscow TK" Budenovskiy "(pav.K7) 160
Moscow TTS" Areal "(Belyaevo) 163
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 15
N.Novgorod SEC" Fantasy " 148
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 87
Omsk TC" Mega " 65
Outbound Trade 0
RostovNaDon Mega shopping center 83
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 19
Samara Melody shopping center 66
Samara ParkHouse shopping center 102
Sergiev Posad 7Ya shopping center 136
St. Petersburg Nevsky Center shopping center 389
St. Petersburg Sennaya shopping center 120
Surgut SEC "City Mall" 167
Tomsk SEC "Emerald City" 35
Tyumen SEC "Crystal" 39
Tyumen SEC "Goodwin" 242
Tyumen TC "Green Coast " 59
Ufa TC" Central " 117
Ufa TC" Family "2 113
Volga TC "Volga Mall" 155
Vologda SEC "Marmelad" 95
Voronezh (Plekhanovskaya, 13) 169
Voronezh SEC "Maksimir" 200
Voronezh SEC City-Park "Grad" 7
Yakutsk Ordzhonikidze, 56 231
Yakutsk TC" Central " 129
Yaroslavl TC" Altair " 52
Zhukovsky st. Chkalov 39m? 48
Zhukovsky st. Chkalov 39m² 1
! Yakutsk Ordzhonikidze, 56 francs 60
! Yakutsk TC "Central" fran 24
item_category_name Accessories - XBOX 360 \
shop_name
Adygea TC "Mega" 317
Balashikha TC "Oktyabr-Kinomir" 243
Chekhov TC" Karnaval " 492
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 132
Kaluga TC "XXI century" 443
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 231
Khimki TC" Mega " 691
Kolomna TC "Rio" 439
Krasnoyarsk TC "June" 368
Krasnoyarsk TC "Vzletka Plaza" 125
Kursk TC " Pushkinskiy " 330
Moscow MTRC" Afi Mall " 266
Moscow Shop C21 121
Moscow TC" MEGA Belaya Dacha II " 988
Moscow TC" MEGA Teply Stan "II 963
Moscow TC" New Age "(Novokosino) 288
Moscow TC" Perlovsky " 362
Moscow TC" Semenovsky " 1096
Moscow TC" Serebryany House " 26
Moscow TEC" Atrium " 888
Moscow TK" Budenovskiy "(pav.A2) 37
Moscow TK" Budenovskiy "(pav.K7) 430
Moscow TTS" Areal "(Belyaevo) 307
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 37
N.Novgorod SEC" Fantasy " 338
N.Novgorod SEC" RIO " 8
Novosibirsk SEC" Gallery Novosibirsk " 2
Novosibirsk SEC" Mega " 208
Omsk TC" Mega " 467
Outbound Trade 21
RostovNaDon Mega shopping center 334
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 57
Samara Melody shopping center 108
Samara ParkHouse shopping center 211
Sergiev Posad 7Ya shopping center 569
St. Petersburg Nevsky Center shopping center 520
St. Petersburg Sennaya shopping center 349
Surgut SEC "City Mall" 355
Tomsk SEC "Emerald City" 179
Tyumen SEC "Crystal" 145
Tyumen SEC "Goodwin" 571
Tyumen TC "Green Coast " 174
Ufa TC" Central " 337
Ufa TC" Family "2 481
Volga TC "Volga Mall" 319
Vologda SEC "Marmelad" 207
Voronezh (Plekhanovskaya, 13) 352
Voronezh SEC "Maksimir" 419
Voronezh SEC City-Park "Grad" 11
Yakutsk Ordzhonikidze, 56 826
Yakutsk TC" Central " 655
Yaroslavl TC" Altair " 247
Zhukovsky st. Chkalov 39m? 138
Zhukovsky st. Chkalov 39m² 6
! Yakutsk Ordzhonikidze, 56 francs 116
! Yakutsk TC "Central" fran 57
item_category_name Accessories - XBOX ONE \
shop_name
Adygea TC "Mega" 68
Balashikha TC "Oktyabr-Kinomir" 60
Chekhov TC" Karnaval " 57
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 161
Kaluga TC "XXI century" 87
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 58
Khimki TC" Mega " 123
Kolomna TC "Rio" 54
Krasnoyarsk TC "June" 77
Krasnoyarsk TC "Vzletka Plaza" 28
Kursk TC " Pushkinskiy " 45
Moscow MTRC" Afi Mall " 153
Moscow Shop C21 74
Moscow TC" MEGA Belaya Dacha II " 206
Moscow TC" MEGA Teply Stan "II 238
Moscow TC" New Age "(Novokosino) 53
Moscow TC" Perlovsky " 34
Moscow TC" Semenovsky " 212
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 289
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 65
Moscow TTS" Areal "(Belyaevo) 119
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 23
N.Novgorod SEC" Fantasy " 131
N.Novgorod SEC" RIO " 40
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 87
Omsk TC" Mega " 122
Outbound Trade 15
RostovNaDon Mega shopping center 46
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 80
Samara Melody shopping center 47
Samara ParkHouse shopping center 56
Sergiev Posad 7Ya shopping center 79
St. Petersburg Nevsky Center shopping center 271
St. Petersburg Sennaya shopping center 72
Surgut SEC "City Mall" 99
Tomsk SEC "Emerald City" 94
Tyumen SEC "Crystal" 56
Tyumen SEC "Goodwin" 71
Tyumen TC "Green Coast " 28
Ufa TC" Central " 56
Ufa TC" Family "2 73
Volga TC "Volga Mall" 45
Vologda SEC "Marmelad" 65
Voronezh (Plekhanovskaya, 13) 92
Voronezh SEC "Maksimir" 63
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 168
Yakutsk TC" Central " 81
Yaroslavl TC" Altair " 72
Zhukovsky st. Chkalov 39m? 36
Zhukovsky st. Chkalov 39m² 1
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Android games - Number \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 91
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 0
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Batteries \
shop_name
Adygea TC "Mega" 34
Balashikha TC "Oktyabr-Kinomir" 36
Chekhov TC" Karnaval " 152
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 2
Kaluga TC "XXI century" 62
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 164
Khimki TC" Mega " 169
Kolomna TC "Rio" 202
Krasnoyarsk TC "June" 122
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 96
Moscow MTRC" Afi Mall " 129
Moscow Shop C21 84
Moscow TC" MEGA Belaya Dacha II " 53
Moscow TC" MEGA Teply Stan "II 411
Moscow TC" New Age "(Novokosino) 108
Moscow TC" Perlovsky " 130
Moscow TC" Semenovsky " 439
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 338
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 44
Moscow TTS" Areal "(Belyaevo) 73
Moscow" Sale " 2
Mytishchi SEC" XL-3 " 29
N.Novgorod SEC" Fantasy " 67
N.Novgorod SEC" RIO " 43
Novosibirsk SEC" Gallery Novosibirsk " 2
Novosibirsk SEC" Mega " 157
Omsk TC" Mega " 74
Outbound Trade 0
RostovNaDon Mega shopping center 75
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 73
Samara Melody shopping center 546
Samara ParkHouse shopping center 136
Sergiev Posad 7Ya shopping center 128
St. Petersburg Nevsky Center shopping center 155
St. Petersburg Sennaya shopping center 155
Surgut SEC "City Mall" 134
Tomsk SEC "Emerald City" 49
Tyumen SEC "Crystal" 36
Tyumen SEC "Goodwin" 241
Tyumen TC "Green Coast " 242
Ufa TC" Central " 183
Ufa TC" Family "2 117
Volga TC "Volga Mall" 50
Vologda SEC "Marmelad" 82
Voronezh (Plekhanovskaya, 13) 392
Voronezh SEC "Maksimir" 110
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 374
Yakutsk TC" Central " 170
Yaroslavl TC" Altair " 347
Zhukovsky st. Chkalov 39m? 89
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 48
! Yakutsk TC "Central" fran 41
item_category_name Blank media (piece) \
shop_name
Adygea TC "Mega" 42
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 310
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 3
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 105
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 120
Kolomna TC "Rio" 186
Krasnoyarsk TC "June" 86
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 101
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 285
Moscow TC" Perlovsky " 266
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 254
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 84
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 67
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 548
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 508
Ufa TC" Central " 291
Ufa TC" Family "2 0
Volga TC "Volga Mall" 208
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 290
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 228
Yakutsk TC" Central " 170
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 110
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 69
! Yakutsk TC "Central" fran 59
item_category_name ... \
shop_name ...
Adygea TC "Mega" ...
Balashikha TC "Oktyabr-Kinomir" ...
Chekhov TC" Karnaval " ...
Digital warehouse 1C-Online ...
Internet-shop of emergency situations ...
Kaluga TC "XXI century" ...
Kazan TC "Behetle" ...
Kazan TC "ParkHouse" II ...
Khimki TC" Mega " ...
Kolomna TC "Rio" ...
Krasnoyarsk TC "June" ...
Krasnoyarsk TC "Vzletka Plaza" ...
Kursk TC " Pushkinskiy " ...
Moscow MTRC" Afi Mall " ...
Moscow Shop C21 ...
Moscow TC" MEGA Belaya Dacha II " ...
Moscow TC" MEGA Teply Stan "II ...
Moscow TC" New Age "(Novokosino) ...
Moscow TC" Perlovsky " ...
Moscow TC" Semenovsky " ...
Moscow TC" Serebryany House " ...
Moscow TEC" Atrium " ...
Moscow TK" Budenovskiy "(pav.A2) ...
Moscow TK" Budenovskiy "(pav.K7) ...
Moscow TTS" Areal "(Belyaevo) ...
Moscow" Sale " ...
Mytishchi SEC" XL-3 " ...
N.Novgorod SEC" Fantasy " ...
N.Novgorod SEC" RIO " ...
Novosibirsk SEC" Gallery Novosibirsk " ...
Novosibirsk SEC" Mega " ...
Omsk TC" Mega " ...
Outbound Trade ...
RostovNaDon Mega shopping center ...
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... ...
RostovNaDonu SEC" Megacenter Horizon ...
Samara Melody shopping center ...
Samara ParkHouse shopping center ...
Sergiev Posad 7Ya shopping center ...
St. Petersburg Nevsky Center shopping center ...
St. Petersburg Sennaya shopping center ...
Surgut SEC "City Mall" ...
Tomsk SEC "Emerald City" ...
Tyumen SEC "Crystal" ...
Tyumen SEC "Goodwin" ...
Tyumen TC "Green Coast " ...
Ufa TC" Central " ...
Ufa TC" Family "2 ...
Volga TC "Volga Mall" ...
Vologda SEC "Marmelad" ...
Voronezh (Plekhanovskaya, 13) ...
Voronezh SEC "Maksimir" ...
Voronezh SEC City-Park "Grad" ...
Yakutsk Ordzhonikidze, 56 ...
Yakutsk TC" Central " ...
Yaroslavl TC" Altair " ...
Zhukovsky st. Chkalov 39m? ...
Zhukovsky st. Chkalov 39m² ...
! Yakutsk Ordzhonikidze, 56 francs ...
! Yakutsk TC "Central" fran ...
item_category_name Programs - 1C: Enterprise 8 \
shop_name
Adygea TC "Mega" 165
Balashikha TC "Oktyabr-Kinomir" 62
Chekhov TC" Karnaval " 136
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 1447
Kaluga TC "XXI century" 115
Kazan TC "Behetle" 8
Kazan TC "ParkHouse" II 128
Khimki TC" Mega " 402
Kolomna TC "Rio" 222
Krasnoyarsk TC "June" 102
Krasnoyarsk TC "Vzletka Plaza" 30
Kursk TC " Pushkinskiy " 113
Moscow MTRC" Afi Mall " 206
Moscow Shop C21 2134
Moscow TC" MEGA Belaya Dacha II " 833
Moscow TC" MEGA Teply Stan "II 387
Moscow TC" New Age "(Novokosino) 260
Moscow TC" Perlovsky " 239
Moscow TC" Semenovsky " 641
Moscow TC" Serebryany House " 52
Moscow TEC" Atrium " 784
Moscow TK" Budenovskiy "(pav.A2) 49
Moscow TK" Budenovskiy "(pav.K7) 520
Moscow TTS" Areal "(Belyaevo) 389
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 9
N.Novgorod SEC" Fantasy " 86
N.Novgorod SEC" RIO " 19
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 171
Omsk TC" Mega " 135
Outbound Trade 0
RostovNaDon Mega shopping center 95
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 27
Samara Melody shopping center 68
Samara ParkHouse shopping center 101
Sergiev Posad 7Ya shopping center 144
St. Petersburg Nevsky Center shopping center 430
St. Petersburg Sennaya shopping center 241
Surgut SEC "City Mall" 183
Tomsk SEC "Emerald City" 26
Tyumen SEC "Crystal" 29
Tyumen SEC "Goodwin" 79
Tyumen TC "Green Coast " 49
Ufa TC" Central " 114
Ufa TC" Family "2 171
Volga TC "Volga Mall" 87
Vologda SEC "Marmelad" 47
Voronezh (Plekhanovskaya, 13) 235
Voronezh SEC "Maksimir" 129
Voronezh SEC City-Park "Grad" 6
Yakutsk Ordzhonikidze, 56 214
Yakutsk TC" Central " 120
Yaroslavl TC" Altair " 106
Zhukovsky st. Chkalov 39m? 130
Zhukovsky st. Chkalov 39m² 3
! Yakutsk Ordzhonikidze, 56 francs 20
! Yakutsk TC "Central" fran 14
item_category_name Programs - Educational \
shop_name
Adygea TC "Mega" 33
Balashikha TC "Oktyabr-Kinomir" 12
Chekhov TC" Karnaval " 126
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 140
Kaluga TC "XXI century" 24
Kazan TC "Behetle" 5
Kazan TC "ParkHouse" II 7
Khimki TC" Mega " 118
Kolomna TC "Rio" 126
Krasnoyarsk TC "June" 60
Krasnoyarsk TC "Vzletka Plaza" 4
Kursk TC " Pushkinskiy " 22
Moscow MTRC" Afi Mall " 52
Moscow Shop C21 543
Moscow TC" MEGA Belaya Dacha II " 5
Moscow TC" MEGA Teply Stan "II 69
Moscow TC" New Age "(Novokosino) 61
Moscow TC" Perlovsky " 49
Moscow TC" Semenovsky " 29
Moscow TC" Serebryany House " 48
Moscow TEC" Atrium " 227
Moscow TK" Budenovskiy "(pav.A2) 54
Moscow TK" Budenovskiy "(pav.K7) 66
Moscow TTS" Areal "(Belyaevo) 12
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 79
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 108
Omsk TC" Mega " 130
Outbound Trade 0
RostovNaDon Mega shopping center 59
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 1
Samara Melody shopping center 89
Samara ParkHouse shopping center 11
Sergiev Posad 7Ya shopping center 25
St. Petersburg Nevsky Center shopping center 30
St. Petersburg Sennaya shopping center 8
Surgut SEC "City Mall" 1
Tomsk SEC "Emerald City" 3
Tyumen SEC "Crystal" 1
Tyumen SEC "Goodwin" 7
Tyumen TC "Green Coast " 85
Ufa TC" Central " 153
Ufa TC" Family "2 8
Volga TC "Volga Mall" 103
Vologda SEC "Marmelad" 12
Voronezh (Plekhanovskaya, 13) 176
Voronezh SEC "Maksimir" 17
Voronezh SEC City-Park "Grad" 20
Yakutsk Ordzhonikidze, 56 324
Yakutsk TC" Central " 172
Yaroslavl TC" Altair " 4
Zhukovsky st. Chkalov 39m? 8
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 104
! Yakutsk TC "Central" fran 61
item_category_name Programs - Educational (Digit) \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 2346
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 0
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Programs - For home and office \
shop_name
Adygea TC "Mega" 481
Balashikha TC "Oktyabr-Kinomir" 152
Chekhov TC" Karnaval " 434
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 417
Kaluga TC "XXI century" 1825
Kazan TC "Behetle" 38
Kazan TC "ParkHouse" II 328
Khimki TC" Mega " 1237
Kolomna TC "Rio" 1087
Krasnoyarsk TC "June" 515
Krasnoyarsk TC "Vzletka Plaza" 192
Kursk TC " Pushkinskiy " 761
Moscow MTRC" Afi Mall " 500
Moscow Shop C21 2773
Moscow TC" MEGA Belaya Dacha II " 1486
Moscow TC" MEGA Teply Stan "II 804
Moscow TC" New Age "(Novokosino) 1694
Moscow TC" Perlovsky " 671
Moscow TC" Semenovsky " 3177
Moscow TC" Serebryany House " 168
Moscow TEC" Atrium " 1556
Moscow TK" Budenovskiy "(pav.A2) 501
Moscow TK" Budenovskiy "(pav.K7) 3780
Moscow TTS" Areal "(Belyaevo) 909
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 25
N.Novgorod SEC" Fantasy " 219
N.Novgorod SEC" RIO " 18
Novosibirsk SEC" Gallery Novosibirsk " 3
Novosibirsk SEC" Mega " 617
Omsk TC" Mega " 509
Outbound Trade 4
RostovNaDon Mega shopping center 603
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 41
Samara Melody shopping center 608
Samara ParkHouse shopping center 388
Sergiev Posad 7Ya shopping center 845
St. Petersburg Nevsky Center shopping center 630
St. Petersburg Sennaya shopping center 330
Surgut SEC "City Mall" 568
Tomsk SEC "Emerald City" 56
Tyumen SEC "Crystal" 115
Tyumen SEC "Goodwin" 740
Tyumen TC "Green Coast " 542
Ufa TC" Central " 464
Ufa TC" Family "2 574
Volga TC "Volga Mall" 718
Vologda SEC "Marmelad" 122
Voronezh (Plekhanovskaya, 13) 1421
Voronezh SEC "Maksimir" 856
Voronezh SEC City-Park "Grad" 35
Yakutsk Ordzhonikidze, 56 2760
Yakutsk TC" Central " 1111
Yaroslavl TC" Altair " 742
Zhukovsky st. Chkalov 39m? 920
Zhukovsky st. Chkalov 39m² 20
! Yakutsk Ordzhonikidze, 56 francs 252
! Yakutsk TC "Central" fran 121
item_category_name Programs - For home and office (Digit) \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 3746
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 0
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Programs - MAC (Digit) \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 56
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 0
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 0
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Service \
shop_name
Adygea TC "Mega" 161
Balashikha TC "Oktyabr-Kinomir" 179
Chekhov TC" Karnaval " 159
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 185
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 178
Khimki TC" Mega " 179
Kolomna TC "Rio" 218
Krasnoyarsk TC "June" 193
Krasnoyarsk TC "Vzletka Plaza" 62
Kursk TC " Pushkinskiy " 176
Moscow MTRC" Afi Mall " 218
Moscow Shop C21 262
Moscow TC" MEGA Belaya Dacha II " 373
Moscow TC" MEGA Teply Stan "II 248
Moscow TC" New Age "(Novokosino) 162
Moscow TC" Perlovsky " 156
Moscow TC" Semenovsky " 478
Moscow TC" Serebryany House " 8
Moscow TEC" Atrium " 288
Moscow TK" Budenovskiy "(pav.A2) 8
Moscow TK" Budenovskiy "(pav.K7) 245
Moscow TTS" Areal "(Belyaevo) 265
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 49
N.Novgorod SEC" Fantasy " 279
N.Novgorod SEC" RIO " 72
Novosibirsk SEC" Gallery Novosibirsk " 1
Novosibirsk SEC" Mega " 129
Omsk TC" Mega " 274
Outbound Trade 4
RostovNaDon Mega shopping center 97
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 4
RostovNaDonu SEC" Megacenter Horizon 104
Samara Melody shopping center 90
Samara ParkHouse shopping center 145
Sergiev Posad 7Ya shopping center 226
St. Petersburg Nevsky Center shopping center 382
St. Petersburg Sennaya shopping center 165
Surgut SEC "City Mall" 212
Tomsk SEC "Emerald City" 195
Tyumen SEC "Crystal" 105
Tyumen SEC "Goodwin" 117
Tyumen TC "Green Coast " 0
Ufa TC" Central " 156
Ufa TC" Family "2 223
Volga TC "Volga Mall" 302
Vologda SEC "Marmelad" 195
Voronezh (Plekhanovskaya, 13) 330
Voronezh SEC "Maksimir" 328
Voronezh SEC City-Park "Grad" 1
Yakutsk Ordzhonikidze, 56 160
Yakutsk TC" Central " 116
Yaroslavl TC" Altair " 205
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Service - Tickets \
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 44
Chekhov TC" Karnaval " 36
Digital warehouse 1C-Online 3
Internet-shop of emergency situations 169
Kaluga TC "XXI century" 46
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 18
Khimki TC" Mega " 0
Kolomna TC "Rio" 73
Krasnoyarsk TC "June" 4
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 20
Moscow MTRC" Afi Mall " 65
Moscow Shop C21 53
Moscow TC" MEGA Belaya Dacha II " 19
Moscow TC" MEGA Teply Stan "II 109
Moscow TC" New Age "(Novokosino) 1
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 134
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 144
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 41
Moscow TTS" Areal "(Belyaevo) 61
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 30
N.Novgorod SEC" RIO " 18
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 2
Omsk TC" Mega " 5
Outbound Trade 0
RostovNaDon Mega shopping center 3
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 9
Samara Melody shopping center 10
Samara ParkHouse shopping center 10
Sergiev Posad 7Ya shopping center 65
St. Petersburg Nevsky Center shopping center 43
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 3
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 10
Tyumen TC "Green Coast " 0
Ufa TC" Central " 2
Ufa TC" Family "2 4
Volga TC "Volga Mall" 2
Vologda SEC "Marmelad" 11
Voronezh (Plekhanovskaya, 13) 32
Voronezh SEC "Maksimir" 13
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 12
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name Tickets (Digital) \
shop_name
Adygea TC "Mega" 3
Balashikha TC "Oktyabr-Kinomir" 35
Chekhov TC" Karnaval " 41
Digital warehouse 1C-Online 507
Internet-shop of emergency situations 111
Kaluga TC "XXI century" 46
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 9
Khimki TC" Mega " 76
Kolomna TC "Rio" 66
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 1
Kursk TC " Pushkinskiy " 21
Moscow MTRC" Afi Mall " 57
Moscow Shop C21 55
Moscow TC" MEGA Belaya Dacha II " 82
Moscow TC" MEGA Teply Stan "II 57
Moscow TC" New Age "(Novokosino) 44
Moscow TC" Perlovsky " 70
Moscow TC" Semenovsky " 114
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 98
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 39
Moscow TTS" Areal "(Belyaevo) 62
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 8
N.Novgorod SEC" Fantasy " 25
N.Novgorod SEC" RIO " 5
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 1
Omsk TC" Mega " 2
Outbound Trade 0
RostovNaDon Mega shopping center 9
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 7
Samara Melody shopping center 4
Samara ParkHouse shopping center 7
Sergiev Posad 7Ya shopping center 52
St. Petersburg Nevsky Center shopping center 26
St. Petersburg Sennaya shopping center 22
Surgut SEC "City Mall" 2
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 2
Tyumen TC "Green Coast " 0
Ufa TC" Central " 3
Ufa TC" Family "2 8
Volga TC "Volga Mall" 2
Vologda SEC "Marmelad" 26
Voronezh (Plekhanovskaya, 13) 25
Voronezh SEC "Maksimir" 8
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 5
Yakutsk TC" Central " 1
Yaroslavl TC" Altair " 27
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 0
! Yakutsk TC "Central" fran 0
item_category_name PC - Headsets / Headphones
shop_name
Adygea TC "Mega" 0
Balashikha TC "Oktyabr-Kinomir" 0
Chekhov TC" Karnaval " 0
Digital warehouse 1C-Online 0
Internet-shop of emergency situations 0
Kaluga TC "XXI century" 0
Kazan TC "Behetle" 0
Kazan TC "ParkHouse" II 0
Khimki TC" Mega " 0
Kolomna TC "Rio" 0
Krasnoyarsk TC "June" 0
Krasnoyarsk TC "Vzletka Plaza" 0
Kursk TC " Pushkinskiy " 0
Moscow MTRC" Afi Mall " 0
Moscow Shop C21 0
Moscow TC" MEGA Belaya Dacha II " 0
Moscow TC" MEGA Teply Stan "II 0
Moscow TC" New Age "(Novokosino) 0
Moscow TC" Perlovsky " 0
Moscow TC" Semenovsky " 0
Moscow TC" Serebryany House " 0
Moscow TEC" Atrium " 1
Moscow TK" Budenovskiy "(pav.A2) 0
Moscow TK" Budenovskiy "(pav.K7) 0
Moscow TTS" Areal "(Belyaevo) 0
Moscow" Sale " 0
Mytishchi SEC" XL-3 " 0
N.Novgorod SEC" Fantasy " 0
N.Novgorod SEC" RIO " 0
Novosibirsk SEC" Gallery Novosibirsk " 0
Novosibirsk SEC" Mega " 0
Omsk TC" Mega " 1
Outbound Trade 0
RostovNaDon Mega shopping center 0
RostovNaDon Megatsentr Gorizont Ostrovnoy shop... 0
RostovNaDonu SEC" Megacenter Horizon 0
Samara Melody shopping center 0
Samara ParkHouse shopping center 0
Sergiev Posad 7Ya shopping center 0
St. Petersburg Nevsky Center shopping center 0
St. Petersburg Sennaya shopping center 0
Surgut SEC "City Mall" 0
Tomsk SEC "Emerald City" 0
Tyumen SEC "Crystal" 0
Tyumen SEC "Goodwin" 0
Tyumen TC "Green Coast " 0
Ufa TC" Central " 0
Ufa TC" Family "2 0
Volga TC "Volga Mall" 0
Vologda SEC "Marmelad" 0
Voronezh (Plekhanovskaya, 13) 0
Voronezh SEC "Maksimir" 0
Voronezh SEC City-Park "Grad" 0
Yakutsk Ordzhonikidze, 56 0
Yakutsk TC" Central " 0
Yaroslavl TC" Altair " 0
Zhukovsky st. Chkalov 39m? 0
Zhukovsky st. Chkalov 39m² 0
! Yakutsk Ordzhonikidze, 56 francs 1
! Yakutsk TC "Central" fran 0
[60 rows x 84 columns]
Frequency of 'Low' price range: 0
item_category_name
Accessories - PS2 215.500000
Accessories - PS3 2255.981900
Accessories - PS4 2105.419083
Accessories - PSP 555.057055
Accessories - PSVita 864.652948
...
Programs - MAC (Digit) 3403.571429
Service 1094.625747
Service - Tickets 1246.023391
Tickets (Digital) 1344.607752
PC - Headsets / Headphones 87.800000
Name: item_price, Length: 84, dtype: float64
#stationarity analysis
# Convert the date column to datetime format
final_dataset['date'] = pd.to_datetime(final_dataset['date'], format='%d.%m.%Y')
monthly_data = final_dataset.groupby(final_dataset['date'].dt.to_period('M')).agg({
'item_cnt_month': 'sum',
}).reset_index()
def adf_test(timeseries):
result = adfuller(timeseries, autolag='AIC')
print('ADF Statistic:', result[0])
print('p-value:', result[1])
print('Critical Values:')
for key, value in result[4].items():
print(f' {key}: {value}')
if result[1] <= 0.05:
print("Stationary (Reject the null hypothesis)")
else:
print("Non-Stationary (Fail to reject the null hypothesis)")
item_cnt_month_series = monthly_data['item_cnt_month']
plt.figure(figsize=(12, 6))
plt.plot(item_cnt_month_series)
plt.title('Monthly Item Count Over Time')
plt.xlabel('Date')
plt.ylabel('Item Count')
plt.show()
adf_test(item_cnt_month_series)
ADF Statistic: -2.372251519825604 p-value: 0.1497218397733845 Critical Values: 1%: -3.6461350877925254 5%: -2.954126991123355 10%: -2.6159676124885216 Non-Stationary (Fail to reject the null hypothesis)
print("\n\nHead of final_dataset:\n")
print(final_dataset.head(20))
print(final_dataset.shape)
print(final_dataset.info())
Head of final_dataset:
date date_num month_name year_num shop_id \
0 2013-01-02 02 1 0 59
1 2013-01-03 03 1 0 25
3 2013-01-06 06 1 0 25
4 2013-01-15 15 1 0 25
5 2013-01-10 10 1 0 25
6 2013-01-02 02 1 0 25
7 2013-01-04 04 1 0 25
8 2013-01-11 11 1 0 25
9 2013-01-03 03 1 0 25
10 2013-01-03 03 1 0 25
11 2013-01-05 05 1 0 25
12 2013-01-07 07 1 0 25
13 2013-01-08 08 1 0 25
14 2013-01-10 10 1 0 25
15 2013-01-11 11 1 0 25
16 2013-01-13 13 1 0 25
17 2013-01-16 16 1 0 25
18 2013-01-26 26 1 0 25
19 2013-01-27 27 1 0 25
20 2013-01-09 09 1 0 25
shop_name item_id \
0 Yaroslavl TC" Altair " 22154
1 Moscow TEC" Atrium " 2552
3 Moscow TEC" Atrium " 2554
4 Moscow TEC" Atrium " 2555
5 Moscow TEC" Atrium " 2564
6 Moscow TEC" Atrium " 2565
7 Moscow TEC" Atrium " 2572
8 Moscow TEC" Atrium " 2572
9 Moscow TEC" Atrium " 2573
10 Moscow TEC" Atrium " 2574
11 Moscow TEC" Atrium " 2574
12 Moscow TEC" Atrium " 2574
13 Moscow TEC" Atrium " 2574
14 Moscow TEC" Atrium " 2574
15 Moscow TEC" Atrium " 2574
16 Moscow TEC" Atrium " 2574
17 Moscow TEC" Atrium " 2574
18 Moscow TEC" Atrium " 2574
19 Moscow TEC" Atrium " 2574
20 Moscow TEC" Atrium " 2593
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
11 DEL REY LANA Born To Die The Paradise Edition 2CD 55
12 DEL REY LANA Born To Die The Paradise Edition 2CD 55
13 DEL REY LANA Born To Die The Paradise Edition 2CD 55
14 DEL REY LANA Born To Die The Paradise Edition 2CD 55
15 DEL REY LANA Born To Die The Paradise Edition 2CD 55
16 DEL REY LANA Born To Die The Paradise Edition 2CD 55
17 DEL REY LANA Born To Die The Paradise Edition 2CD 55
18 DEL REY LANA Born To Die The Paradise Edition 2CD 55
19 DEL REY LANA Born To Die The Paradise Edition 2CD 55
20 DEPECHE MODE Music For The Masses 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
11 Music - CD of local production 399.00 1 399.00
12 Music - CD of local production 399.00 1 399.00
13 Music - CD of local production 399.00 2 798.00
14 Music - CD of local production 399.00 1 399.00
15 Music - CD of local production 399.00 2 798.00
16 Music - CD of local production 399.00 1 399.00
17 Music - CD of local production 399.00 1 399.00
18 Music - CD of local production 399.00 1 399.00
19 Music - CD of local production 399.00 1 399.00
20 Music - CD of local production 279.00 1 279.00
price_range log_revenue scaled_revenue
0 900-100000 6.906755 0.000546
1 800-900 6.801283 0.000491
3 900-100000 7.443693 0.000934
4 900-100000 7.002156 0.000601
5 300-400 5.855072 0.000191
6 500-600 6.308098 0.000300
7 200-300 5.476464 0.000131
8 200-300 5.700444 0.000163
9 200-300 6.799056 0.000490
10 300-400 6.682109 0.000436
11 300-400 5.988961 0.000218
12 300-400 5.988961 0.000218
13 300-400 6.682109 0.000436
14 300-400 5.988961 0.000218
15 300-400 6.682109 0.000436
16 300-400 5.988961 0.000218
17 300-400 5.988961 0.000218
18 300-400 5.988961 0.000218
19 300-400 5.988961 0.000218
20 200-300 5.631212 0.000152
(2928483, 16)
<class 'pandas.core.frame.DataFrame'>
Index: 2928483 entries, 0 to 2935848
Data columns (total 16 columns):
# Column Dtype
--- ------ -----
0 date datetime64[ns]
1 date_num object
2 month_name int64
3 year_num int64
4 shop_id int64
5 shop_name object
6 item_id int64
7 item_name object
8 category_id int64
9 item_category_name object
10 item_price float64
11 item_cnt_month int64
12 revenue float64
13 price_range category
14 log_revenue float64
15 scaled_revenue float64
dtypes: category(1), datetime64[ns](1), float64(4), int64(6), object(4)
memory usage: 360.3+ MB
None
#export the final dataset to csv file
final_dataset.to_csv('./data-set/output/final_dataset_with_cleaning.csv', index=False)
#prepare the data for modeling
df = pd.read_csv('./data-set/sales_train.csv')
#rename item_cnt_day column
df.rename(columns={'item_cnt_day': 'item_count'}, inplace=True)
#removes duplicates
df.drop_duplicates(inplace=True)
#outlier treatment
df = df[(df['item_count'] > 0) & (df['item_count'] < 307980)]
df = df[df['item_count'] < 1000]
#handles incorrect data
df = df[(df['item_price'] > 0) & (df['item_price'] < 100000)]
#converts date column to datetime format
df['date'] = pd.to_datetime(df['date'], format='%d.%m.%Y')
#convert date to year-month format
df['year-month'] = df['date'].dt.strftime('%Y-%m')
#drop date column and item_price column
df.drop(columns=['date', 'item_price'], inplace=True)
# group features
df_train_group = df.groupby(['year-month', 'shop_id', 'item_id']).sum().reset_index()
# pivot table
df = df_train_group.pivot_table(index=['shop_id', 'item_id'], columns='year-month', values='item_count', fill_value=0).reset_index()
print(df.head(10))
print(df.shape)
print(df.info())
year-month shop_id item_id 2013-01 2013-02 2013-03 2013-04 2013-05 \ 0 0 30 0 31 0 0 0 1 0 31 0 11 0 0 0 2 0 32 6 10 0 0 0 3 0 33 3 3 0 0 0 4 0 35 1 14 0 0 0 5 0 36 0 1 0 0 0 6 0 40 0 1 0 0 0 7 0 42 0 1 0 0 0 8 0 43 1 0 0 0 0 9 0 49 0 2 0 0 0 year-month 2013-06 2013-07 2013-08 ... 2015-01 2015-02 2015-03 \ 0 0 0 0 ... 0 0 0 1 0 0 0 ... 0 0 0 2 0 0 0 ... 0 0 0 3 0 0 0 ... 0 0 0 4 0 0 0 ... 0 0 0 5 0 0 0 ... 0 0 0 6 0 0 0 ... 0 0 0 7 0 0 0 ... 0 0 0 8 0 0 0 ... 0 0 0 9 0 0 0 ... 0 0 0 year-month 2015-04 2015-05 2015-06 2015-07 2015-08 2015-09 2015-10 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 [10 rows x 36 columns] (424097, 36) <class 'pandas.core.frame.DataFrame'> RangeIndex: 424097 entries, 0 to 424096 Data columns (total 36 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 shop_id 424097 non-null int64 1 item_id 424097 non-null int64 2 2013-01 424097 non-null int64 3 2013-02 424097 non-null int64 4 2013-03 424097 non-null int64 5 2013-04 424097 non-null int64 6 2013-05 424097 non-null int64 7 2013-06 424097 non-null int64 8 2013-07 424097 non-null int64 9 2013-08 424097 non-null int64 10 2013-09 424097 non-null int64 11 2013-10 424097 non-null int64 12 2013-11 424097 non-null int64 13 2013-12 424097 non-null int64 14 2014-01 424097 non-null int64 15 2014-02 424097 non-null int64 16 2014-03 424097 non-null int64 17 2014-04 424097 non-null int64 18 2014-05 424097 non-null int64 19 2014-06 424097 non-null int64 20 2014-07 424097 non-null int64 21 2014-08 424097 non-null int64 22 2014-09 424097 non-null int64 23 2014-10 424097 non-null int64 24 2014-11 424097 non-null int64 25 2014-12 424097 non-null int64 26 2015-01 424097 non-null int64 27 2015-02 424097 non-null int64 28 2015-03 424097 non-null int64 29 2015-04 424097 non-null int64 30 2015-05 424097 non-null int64 31 2015-06 424097 non-null int64 32 2015-07 424097 non-null int64 33 2015-08 424097 non-null int64 34 2015-09 424097 non-null int64 35 2015-10 424097 non-null int64 dtypes: int64(36) memory usage: 116.5 MB None
#export the final dataset to csv file
final_dataset.to_csv('./data-set/output/dataset_for_modeling.csv', index=False)
# Create X and y variables for train and test sets
X = df[df.columns[:-1]]
y = df[df.columns[-1]]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(339277, 35) (84820, 35) (339277,) (84820,)
#creating evaluation metrics
scores_and_names = []
# Create a function to evaluate the model
def evaluate_the_model(y_true, y_pred, model_name, model):
# Calculate the MAE
mae = mean_absolute_error(y_true, y_pred)
print(f"MAE for {model_name}: {mae:.5f}")
# Calculate the MSE
mse = mean_squared_error(y_true, y_pred)
print(f"MSE for {model_name}: {mse:.5f}")
# Calculate the RMSE
rmse = np.sqrt(mse)
print(f"RMSE for {model_name}: {rmse:.5f}")
# Plot the predictions vs. the actual values
plt.figure(figsize=(12, 6))
plt.plot(y_true, label='Actual Values')
plt.plot(y_pred, label='Predicted Values')
plt.title(f'Predictions vs. Actual Values ({model_name})')
plt.xlabel('Observation')
plt.ylabel('Item Count')
plt.legend()
plt.show()
scores_and_names.append((model_name, rmse))
# Create a Linear Regression model
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
y_pred = lin_reg.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Linear Regression', lin_reg)
MAE for Linear Regression: 0.29022 MSE for Linear Regression: 2.39626 RMSE for Linear Regression: 1.54798
# Create a logistic regression model
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
y_pred = log_reg.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Logistic Regression', log_reg)
C:\Users\srume\AppData\Roaming\Python\Python311\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
MAE for Logistic Regression: 0.16065 MSE for Logistic Regression: 2.61462 RMSE for Logistic Regression: 1.61698
# create a support vector machine model
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Support Vector Machine', svm)
MAE for Support Vector Machine: 0.16065 MSE for Support Vector Machine: 2.61462 RMSE for Support Vector Machine: 1.61698
# create a decision tree model
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Decision Tree', dt)
MAE for Decision Tree: 0.17817 MSE for Decision Tree: 1.88140 RMSE for Decision Tree: 1.37164
# create a random forest model
rf = RandomForestRegressor()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Random Forest', rf)
MAE for Random Forest: 0.16314 MSE for Random Forest: 1.13545 RMSE for Random Forest: 1.06558
#create a stochastic gradient descent model
sgd_reg = SGDRegressor()
sgd_reg.fit(X_train, y_train)
y_pred = sgd_reg.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Stochastic Gradient Descent', sgd_reg)
MAE for Stochastic Gradient Descent: 735578716284226.25000 MSE for Stochastic Gradient Descent: 698048155002567956219484438528.00000 RMSE for Stochastic Gradient Descent: 835492761789453.12500
#create a extra trees model
et = ExtraTreesRegressor()
et.fit(X_train, y_train)
y_pred = et.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Extra Trees', et)
MAE for Extra Trees: 0.17591 MSE for Extra Trees: 1.14391 RMSE for Extra Trees: 1.06954
#create a xgboost model
xgb = XGBRegressor()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
evaluate_the_model(y_test, y_pred, 'XGBoost', xgb)
MAE for XGBoost: 0.21046 MSE for XGBoost: 1.69069 RMSE for XGBoost: 1.30027
#create ridge regression model
ridge = Ridge()
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Ridge Regression', ridge)
MAE for Ridge Regression: 0.29022 MSE for Ridge Regression: 2.39625 RMSE for Ridge Regression: 1.54798
#create lasso regression model
lasso = Lasso()
lasso.fit(X_train, y_train)
y_pred = lasso.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Lasso Regression', lasso)
MAE for Lasso Regression: 0.26487 MSE for Lasso Regression: 2.11585 RMSE for Lasso Regression: 1.45460
#create ARIMA model
arima = ARIMA(y_train, order=(1, 1, 1))
model = arima.fit()
y_pred = model.predict(start=len(y_train), end=len(y_train) + len(X_test) - 1, exog=X_test)
evaluate_the_model(y_test, y_pred, 'ARIMA', arima)
c:\Users\srume\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) c:\Users\srume\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) c:\Users\srume\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) c:\Users\srume\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( c:\Users\srume\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception. return get_prediction_index(
MAE for ARIMA: 0.30035 MSE for ARIMA: 2.58882 RMSE for ARIMA: 1.60898
#create adaboost model
ada = AdaBoostRegressor()
ada.fit(X_train, y_train)
y_pred = ada.predict(X_test)
evaluate_the_model(y_test, y_pred, 'AdaBoost', ada)
MAE for AdaBoost: 0.63456 MSE for AdaBoost: 2.36423 RMSE for AdaBoost: 1.53760
# create bayesian ridge model
br = BayesianRidge()
br.fit(X_train, y_train)
y_pred = br.predict(X_test)
evaluate_the_model(y_test, y_pred, 'Bayesian Ridge', br)
MAE for Bayesian Ridge: 0.29018 MSE for Bayesian Ridge: 2.39567 RMSE for Bayesian Ridge: 1.54780
# create a knn model
knn = KNeighborsRegressor(n_neighbors=5)
knn.fit(X_train.values, y_train.values)
y_pred = knn.predict(X_test.values)
evaluate_the_model(y_test.values, y_pred, 'K-Nearest Neighbors', knn)
MAE for K-Nearest Neighbors: 0.18599 MSE for K-Nearest Neighbors: 1.60036 RMSE for K-Nearest Neighbors: 1.26505
results = pd.DataFrame(scores_and_names, columns=['Model', 'RMSE'])
#sort the results
results.sort_values(by='RMSE', ascending=True, inplace=True)
#print the results in tabel format
print(results)
Model RMSE 4 Random Forest 1.065577e+00 6 Extra Trees 1.069535e+00 13 K-Nearest Neighbors 1.265051e+00 7 XGBoost 1.300267e+00 3 Decision Tree 1.371640e+00 9 Lasso Regression 1.454596e+00 11 AdaBoost 1.537605e+00 12 Bayesian Ridge 1.547795e+00 8 Ridge Regression 1.547984e+00 0 Linear Regression 1.547985e+00 10 ARIMA 1.608982e+00 1 Logistic Regression 1.616978e+00 2 Support Vector Machine 1.616978e+00 5 Stochastic Gradient Descent 8.354928e+14
#line chart
plt.figure(figsize=(12, 6))
plt.plot(final_dataset['date'], final_dataset['revenue'])
plt.title('Revenue Over Time')
plt.xlabel('Date')
plt.ylabel('Revenue')
plt.show()
#bar chart
plt.figure(figsize=(12, 6))
plt.bar(final_dataset['shop_name'], final_dataset['revenue'])
plt.title('Revenue by Shop')
plt.xlabel('Shop Name')
plt.ylabel('Revenue')
plt.xticks(rotation=90)
plt.show()
#pairplot
sns.pairplot(final_dataset[['item_price', 'item_cnt_month', 'revenue']])
plt.show()
c:\Users\srume\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
#boxplot
plt.figure(figsize=(12, 6))
sns.boxplot(data=final_dataset, x='item_category_name', y='revenue')
plt.title('Revenue by Item Category')
plt.xlabel('Item Category')
plt.ylabel('Revenue')
plt.xticks(rotation=90)
plt.show()
#scatter chart
plt.figure(figsize=(12, 6))
sns.scatterplot(data=final_dataset, x='item_price', y='revenue')
plt.title('Revenue vs. Item Price')
plt.xlabel('Item Price')
plt.ylabel('Revenue')
plt.show()
#histogram
plt.figure(figsize=(12, 6))
sns.histplot(data=final_dataset, x='item_price', kde=True, bins=20)
plt.title('Distribution of Item Count Per Month')
plt.xlabel('Item Count')
plt.ylabel('Frequency')
plt.show()
#area plot
plt.figure(figsize=(12, 6))
plt.stackplot(final_dataset['date'], final_dataset['revenue'])
plt.title('Revenue Over Time')
plt.xlabel('Date')
plt.ylabel('Revenue')
plt.show()
#heatmap
plt.figure(figsize=(12, 6))
sns.heatmap(numeric_columns.corr(), annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap')
plt.show()
# Time Series Plot
plt.figure(figsize=(10, 6))
final_dataset['date'] = pd.to_datetime(final_dataset['date'])
sns.lineplot(x='date', y='revenue', data=final_dataset)
plt.title('Time Series Plot of Revenue')
plt.xlabel('Date')
plt.ylabel('Revenue')
plt.xticks(rotation=45)
plt.show()
print(final_dataset.head(10))
print(final_dataset.shape)
print(final_dataset.info())
date date_num month_name year_num shop_id \
0 2013-01-02 02 1 0 59
1 2013-01-03 03 1 0 25
3 2013-01-06 06 1 0 25
4 2013-01-15 15 1 0 25
5 2013-01-10 10 1 0 25
6 2013-01-02 02 1 0 25
7 2013-01-04 04 1 0 25
8 2013-01-11 11 1 0 25
9 2013-01-03 03 1 0 25
10 2013-01-03 03 1 0 25
shop_name item_id \
0 Yaroslavl TC" Altair " 22154
1 Moscow TEC" Atrium " 2552
3 Moscow TEC" Atrium " 2554
4 Moscow TEC" Atrium " 2555
5 Moscow TEC" Atrium " 2564
6 Moscow TEC" Atrium " 2565
7 Moscow TEC" Atrium " 2572
8 Moscow TEC" Atrium " 2572
9 Moscow TEC" Atrium " 2573
10 Moscow TEC" Atrium " 2574
item_name category_id \
0 SCENE 2012 (BD) 37
1 DEEP PURPLE The House Of Blue Light LP 58
3 DEEP PURPLE Who Do You Think We Are LP 58
4 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 DEEP PURPLE Stormbringer (firms). 56
7 DEFTONES Koi No Yokan 55
8 DEFTONES Koi No Yokan 55
9 DEL REY LANA Born To Die 55
10 DEL REY LANA Born To Die The Paradise Edition 2CD 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
price_range log_revenue scaled_revenue
0 900-100000 6.906755 0.000546
1 800-900 6.801283 0.000491
3 900-100000 7.443693 0.000934
4 900-100000 7.002156 0.000601
5 300-400 5.855072 0.000191
6 500-600 6.308098 0.000300
7 200-300 5.476464 0.000131
8 200-300 5.700444 0.000163
9 200-300 6.799056 0.000490
10 300-400 6.682109 0.000436
(2928483, 16)
<class 'pandas.core.frame.DataFrame'>
Index: 2928483 entries, 0 to 2935848
Data columns (total 16 columns):
# Column Dtype
--- ------ -----
0 date datetime64[ns]
1 date_num object
2 month_name int64
3 year_num int64
4 shop_id int64
5 shop_name object
6 item_id int64
7 item_name object
8 category_id int64
9 item_category_name object
10 item_price float64
11 item_cnt_month int64
12 revenue float64
13 price_range category
14 log_revenue float64
15 scaled_revenue float64
dtypes: category(1), datetime64[ns](1), float64(4), int64(6), object(4)
memory usage: 360.3+ MB
None
#decoding year_num column and month_name column
final_dataset['year_num'] = final_dataset['year_num'].replace({0: '2013', 1: '2014', 2: '2015'})
final_dataset['month_name'] = final_dataset['month_name'].replace({1: 'January', 2: 'February', 3: 'March', 4: 'April', 5: 'May', 6:'June', 7: 'July', 8: 'August', 9: 'September', 10: 'October', 11:'November', 12: 'December'})
print(final_dataset.head(10))
print(final_dataset.tail(10))
date date_num month_name year_num shop_id shop_name \
0 2013-01-02 02 January 2013 59 Yaroslavl TC" Altair "
1 2013-01-03 03 January 2013 25 Moscow TEC" Atrium "
3 2013-01-06 06 January 2013 25 Moscow TEC" Atrium "
4 2013-01-15 15 January 2013 25 Moscow TEC" Atrium "
5 2013-01-10 10 January 2013 25 Moscow TEC" Atrium "
6 2013-01-02 02 January 2013 25 Moscow TEC" Atrium "
7 2013-01-04 04 January 2013 25 Moscow TEC" Atrium "
8 2013-01-11 11 January 2013 25 Moscow TEC" Atrium "
9 2013-01-03 03 January 2013 25 Moscow TEC" Atrium "
10 2013-01-03 03 January 2013 25 Moscow TEC" Atrium "
item_id item_name category_id \
0 22154 SCENE 2012 (BD) 37
1 2552 DEEP PURPLE The House Of Blue Light LP 58
3 2554 DEEP PURPLE Who Do You Think We Are LP 58
4 2555 DEEP PURPLE 30 Very Best Of 2CD (Businesses). 56
5 2564 DEEP PURPLE Perihelion: Live In Concert DVD (C... 59
6 2565 DEEP PURPLE Stormbringer (firms). 56
7 2572 DEFTONES Koi No Yokan 55
8 2572 DEFTONES Koi No Yokan 55
9 2573 DEL REY LANA Born To Die 55
10 2574 DEL REY LANA Born To Die The Paradise Edition 2CD 55
item_category_name item_price item_cnt_month revenue \
0 Cinema - Blu-Ray 999.00 1 999.00
1 Music - Vinyl 899.00 1 899.00
3 Music - Vinyl 1709.05 1 1709.05
4 Music - CD of corporate production 1099.00 1 1099.00
5 Music - Music video 349.00 1 349.00
6 Music - CD of corporate production 549.00 1 549.00
7 Music - CD of local production 239.00 1 239.00
8 Music - CD of local production 299.00 1 299.00
9 Music - CD of local production 299.00 3 897.00
10 Music - CD of local production 399.00 2 798.00
price_range log_revenue scaled_revenue
0 900-100000 6.906755 0.000546
1 800-900 6.801283 0.000491
3 900-100000 7.443693 0.000934
4 900-100000 7.002156 0.000601
5 300-400 5.855072 0.000191
6 500-600 6.308098 0.000300
7 200-300 5.476464 0.000131
8 200-300 5.700444 0.000163
9 200-300 6.799056 0.000490
10 300-400 6.682109 0.000436
date date_num month_name year_num shop_id \
2935839 2015-10-24 24 October 2015 25
2935840 2015-10-31 31 October 2015 25
2935841 2015-10-11 11 October 2015 25
2935842 2015-10-10 10 October 2015 25
2935843 2015-10-09 09 October 2015 25
2935844 2015-10-10 10 October 2015 25
2935845 2015-10-09 09 October 2015 25
2935846 2015-10-14 14 October 2015 25
2935847 2015-10-22 22 October 2015 25
2935848 2015-10-03 03 October 2015 25
shop_name item_id \
2935839 Moscow TEC" Atrium " 7315
2935840 Moscow TEC" Atrium " 7409
2935841 Moscow TEC" Atrium " 7393
2935842 Moscow TEC" Atrium " 7384
2935843 Moscow TEC" Atrium " 7409
2935844 Moscow TEC" Atrium " 7409
2935845 Moscow TEC" Atrium " 7460
2935846 Moscow TEC" Atrium " 7459
2935847 Moscow TEC" Atrium " 7440
2935848 Moscow TEC" Atrium " 7460
item_name category_id \
2935839 V/A Dance Kick! 2CD (digipack) 55
2935840 V/A Nu Jazz Selection (digipack) 55
2935841 V/A Lounge Del Mar 3 2CD (digipack) 55
2935842 V/A Ladies Sing The Blues 3CD 55
2935843 V/A Nu Jazz Selection (digipack) 55
2935844 V/A Nu Jazz Selection (digipack) 55
2935845 V/A The Golden Jazz Collection 1 2CD 55
2935846 V/A The Best Of The 3 Tenors 55
2935847 V/A Relax Collection Planet MP3 (mp3-CD) (jewel) 57
2935848 V/A The Golden Jazz Collection 1 2CD 55
item_category_name item_price item_cnt_month revenue \
2935839 Music - CD of local production 399.0 1 399.0
2935840 Music - CD of local production 299.0 1 299.0
2935841 Music - CD of local production 349.0 1 349.0
2935842 Music - CD of local production 749.0 1 749.0
2935843 Music - CD of local production 299.0 1 299.0
2935844 Music - CD of local production 299.0 1 299.0
2935845 Music - CD of local production 299.0 1 299.0
2935846 Music - CD of local production 349.0 1 349.0
2935847 Music - MP3 299.0 1 299.0
2935848 Music - CD of local production 299.0 1 299.0
price_range log_revenue scaled_revenue
2935839 300-400 5.988961 0.000218
2935840 200-300 5.700444 0.000163
2935841 300-400 5.855072 0.000191
2935842 700-800 6.618739 0.000409
2935843 200-300 5.700444 0.000163
2935844 200-300 5.700444 0.000163
2935845 200-300 5.700444 0.000163
2935846 300-400 5.855072 0.000191
2935847 200-300 5.700444 0.000163
2935848 200-300 5.700444 0.000163
#make data frame for the dashboard interactive
df_interactive = final_dataset.interactive()
#creates menu button for the dashboard to select years
year_num_menu = pn.widgets.Select(name='Year', options=df_interactive['year_num'].unique().tolist(), value='2015')
updated_year_df = df_interactive[df_interactive['year_num'] == year_num_menu]
#montly revenue
update_monthly_revenue = (updated_year_df.groupby('month_name')['revenue'].mean().to_frame().reset_index().sort_values(by='month_name').reset_index(drop=True))
update_monthly_revenue_plot = update_monthly_revenue.hvplot.bar(x='month_name', y='revenue', rot=90, title='Average Revenue Per Month by year')
#monthly item count
update_monthly_item_count = (updated_year_df.groupby('month_name')['item_cnt_month'].mean().to_frame().reset_index().sort_values(by='month_name').reset_index(drop=True))
update_monthly_item_count_pie = update_monthly_item_count.plot(kind='pie', y='item_cnt_month', label='month_name', autopct='%1.1f%%', title='Average Item Count Per Month by year')
#sales by category
update_sales_by_category = (updated_year_df.groupby('item_category_name')['revenue'].mean().to_frame().reset_index().sort_values(by='revenue').reset_index(drop=True))
update_sales_by_category_plot = update_sales_by_category.hvplot.area(x='item_category_name', y='revenue', rot=90, title='Average Revenue Per Category by year', height=500, width=1000)
#sales by shop
update_sales_by_shop = (updated_year_df.groupby('shop_name')['revenue'].mean().to_frame().reset_index().sort_values(by='revenue').reset_index(drop=True))
update_sales_by_shop_plot = update_sales_by_shop.hvplot.area(x='shop_name', y='revenue', rot=90, title='Average Revenue Per Shop by year', height=500, width=1000)
#best selling category in all year
update_best_selling_category = (df_interactive.groupby('item_category_name')['item_cnt_month'].mean().to_frame().reset_index().sort_values(by='item_cnt_month', ascending=False).reset_index(drop=True))
update_best_selling_category_plot = update_best_selling_category.hvplot.barh(x='item_category_name', y='item_cnt_month', rot=90, title='Average Item Count Per Category', height=1000, width=1000)
#creates echarts bar for the dashboard to display revenue by year
revenue_by_year_bar = { 'title' : { 'text' : 'Revenue by Year' }, 'tooltip' : { 'trigger': 'axis' }, 'legend': { 'data': ['Revenue'] }, 'xAxis' : { 'data' : final_dataset['year_num'].unique().tolist() }, 'yAxis' : { }, 'series' : [{ 'name' : 'Revenue', 'type' : 'bar', 'data' : final_dataset.groupby('year_num')['revenue'].sum().tolist() }] }
revenue_by_year_echart_pane = pn.pane.ECharts(revenue_by_year_bar, width=400, height=400)
template = pn.template.FastListTemplate(
title='Grocerry Sales Dashboard',
sidebar=[pn.pane.Markdown("# Revenue by Year"),
revenue_by_year_echart_pane,
pn.pane.Markdown("#### Select Year"),
year_num_menu],
main=[pn.Row(pn.Column(update_monthly_revenue_plot.panel(width=500)),
pn.Column(update_monthly_item_count_pie.panel(width=500), margin=(0,25)),
),
pn.Row(update_sales_by_category_plot),
pn.Row(update_sales_by_shop_plot),
pn.Row(update_best_selling_category_plot)],
accent_base_color="#88d8b0",
header_background="#88d8b0",
)
template.show()
Launching server at http://localhost:53043
<panel.io.server.Server at 0x1270fb93150>
WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='75fa204f-25bf-4df4-950f-c52628c0f79e', ...)